{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C99P9OgY5NcH"
      },
      "source": [
        "# cifar10_resnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:32.363026Z",
          "start_time": "2025-06-26T01:43:29.447990Z"
        },
        "id": "CTgIWCMM5NcO"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import datasets, transforms\n",
        "from deeplearning_train import EarlyStopping, ModelSaver,train_classification_model,plot_learning_curves\n",
        "from deeplearning_train import evaluate_classification_model as evaluate_model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "IHGsTDwF5NcO"
      },
      "outputs": [],
      "source": [
        "# # 加载torchvision自带的resnet18模型\n",
        "# resnet18 = torchvision.models.resnet18()  # 加载resnet18模型\n",
        "\n",
        "# # 打印模型结构\n",
        "# print(resnet18)  # 输出模型的结构信息\n",
        "\n",
        "# from torchinfo import summary  # 导入summary函数用于显示模型结构信息\n",
        "# vgg16 = torchvision.models.vgg16()  # 加载vgg16模型\n",
        "\n",
        "# summary(vgg16, input_size=(1, 3, 224, 224), col_names=[\"input_size\", \"output_size\", \"num_params\", \"params_percent\"])  # 使用summary显示vgg16详细信息\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UOZ_jNWg5NcP"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "token = {\"username\":\"zhangyudataset\",\"key\":\"6ae9a985be19950353520e31297702b4\"}\n",
        "with open('/content/kaggle.json', 'w') as file:\n",
        "  json.dump(token, file)  # json.dump类似于write，直接把字典类型数据变为字符串写入文件\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRuuAxVn5NcP",
        "outputId": "dc937255-f3ab-4f4b-aab5-a6d4b4cc5ee8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "- path is now set to: /content\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp /content/kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle config set -n path -v /content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c8f40gk5NcP",
        "outputId": "ce3a1d3e-437f-42e0-8c6e-fbaa0914c5e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cifar-10.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "# 需要先参加比赛才能下载数据集\n",
        "!kaggle competitions download -c cifar-10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "aa-IOOlO5NcQ",
        "outputId": "6d9b983a-fe7a-40ac-a3bf-d7310fd4472a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/competitions/cifar-10/cifar-10.zip\n",
            "replace sampleSubmission.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: sampleSubmission.csv    \n",
            "replace test.7z? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: test.7z                 \n",
            "replace train.7z? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: train.7z                \n",
            "replace trainLabels.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: trainLabels.csv         \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/competitions/cifar-10/cifar-10.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "B7One41g5NcQ",
        "outputId": "6a8e4a5f-b6b6-4081-ce84-8c48fab73ca6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: py7zr in /usr/local/lib/python3.11/dist-packages (1.0.0)\n",
            "Requirement already satisfied: texttable in /usr/local/lib/python3.11/dist-packages (from py7zr) (1.7.0)\n",
            "Requirement already satisfied: pycryptodomex>=3.20.0 in /usr/local/lib/python3.11/dist-packages (from py7zr) (3.23.0)\n",
            "Requirement already satisfied: brotli>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from py7zr) (1.1.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from py7zr) (5.9.5)\n",
            "Requirement already satisfied: pyzstd>=0.16.1 in /usr/local/lib/python3.11/dist-packages (from py7zr) (0.17.0)\n",
            "Requirement already satisfied: pyppmd<1.3.0,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from py7zr) (1.2.0)\n",
            "Requirement already satisfied: pybcj<1.1.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from py7zr) (1.0.6)\n",
            "Requirement already satisfied: multivolumefile>=0.2.3 in /usr/local/lib/python3.11/dist-packages (from py7zr) (0.2.3)\n",
            "Requirement already satisfied: inflate64<1.1.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from py7zr) (1.0.3)\n",
            "Requirement already satisfied: typing-extensions>=4.13.2 in /usr/local/lib/python3.11/dist-packages (from pyzstd>=0.16.1->py7zr) (4.14.0)\n"
          ]
        }
      ],
      "source": [
        "# 安装py7zr库，用于解压7z格式的压缩包\n",
        "%pip install py7zr  # 在Jupyter环境下安装py7zr库\n",
        "\n",
        "# 导入py7zr库\n",
        "import py7zr  # 导入py7zr模块以便后续解压操作\n",
        "\n",
        "# 创建一个SevenZipFile对象，打开'./train.7z'文件，模式为只读\n",
        "a = py7zr.SevenZipFile(r'./train.7z', 'r')  # 用于读取7z压缩包\n",
        "\n",
        "# 将压缩包中的内容全部解压到指定目录'./competitions/cifar-10/'\n",
        "a.extractall(path=r'./competitions/cifar-10/')  # 解压所有文件到目标文件夹\n",
        "\n",
        "# 关闭SevenZipFile对象，释放资源\n",
        "a.close()  # 关闭文件，完成解压流程"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pazq-6NB5NcQ"
      },
      "source": [
        "# 把数据集划分为训练集45000和验证集5000，并给DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.144223Z",
          "start_time": "2025-06-26T01:43:33.135368Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "leP4jIbc5NcR",
        "outputId": "778e857e-f44c-4235-8a20-3d1b13ecc949"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "完整数据集大小: 50000\n",
            "训练集大小: 45000\n",
            "验证集大小: 5000\n"
          ]
        }
      ],
      "source": [
        "# 加载CIFAR-10数据集相关库\n",
        "import os  # 导入os模块用于文件路径操作\n",
        "import pandas as pd  # 导入pandas用于读取csv文件\n",
        "from PIL import Image  # 导入PIL库用于图片处理\n",
        "from torch.utils.data import Dataset  # 导入PyTorch的数据集基类\n",
        "\n",
        "# 定义CIFAR-10数据集类，继承自Dataset\n",
        "class CIFAR10Dataset(Dataset):\n",
        "    def __init__(self, img_dir, labels_file, transform=None):  # 构造函数，接收图片文件夹、标签文件和预处理方法\n",
        "        self.img_dir = img_dir  # 保存图片文件夹路径\n",
        "        self.transform = transform  # 保存预处理方法\n",
        "\n",
        "        # 读取标签文件，read_csv默认第一行为列名\n",
        "        self.labels_df = pd.read_csv(labels_file)  # 读取csv标签文件\n",
        "        self.img_names = self.labels_df.iloc[:, 0].values.astype(str)  # 获取第一列图片名，转为字符串数组\n",
        "\n",
        "        # 定义类别名称到数字的映射字典\n",
        "        self.class_names_dict = {'airplane': 0, 'automobile': 1, 'bird': 2, 'cat': 3,\n",
        "                                 'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8, 'truck': 9}  # 类别映射字典\n",
        "        # 将文本标签转换为数字ID\n",
        "        self.labels = [self.class_names_dict[label] for label in self.labels_df.iloc[:, 1].values]  # 标签转数字\n",
        "\n",
        "    def __len__(self):  # 返回数据集大小\n",
        "        return len(self.labels)  # 返回标签数量\n",
        "\n",
        "    def __getitem__(self, idx):  # 获取指定索引的数据\n",
        "        img_path = os.path.join(self.img_dir, self.img_names[idx] + '.png')  # 拼接图片路径\n",
        "        image = Image.open(img_path)  # 打开图片\n",
        "        label = self.labels[idx]  # 获取标签\n",
        "\n",
        "        if self.transform:  # 如果有预处理\n",
        "            image_tensor = self.transform(image)  # 对图片做预处理\n",
        "\n",
        "        return image_tensor, label  # 返回图片张量和标签\n",
        "\n",
        "# 定义数据预处理流程\n",
        "transform = transforms.Compose([  # 使用Compose组合多种预处理\n",
        "    transforms.ToTensor(),  # 转为张量\n",
        "    transforms.Normalize((0.4917, 0.4823, 0.4467), (0.2024, 0.1995, 0.2010))  # 标准化\n",
        "])\n",
        "\n",
        "# 加载CIFAR-10数据集\n",
        "img_dir = r\"competitions/cifar-10/train\"  # 图片文件夹路径\n",
        "labels_file = r\"./trainLabels.csv\"  # 标签文件路径\n",
        "full_dataset = CIFAR10Dataset(img_dir=img_dir, labels_file=labels_file, transform=transform)  # 创建完整数据集对象\n",
        "\n",
        "# 定义类别名称列表\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']  # 类别名称\n",
        "\n",
        "# 划分训练集和验证集\n",
        "train_size = 45000  # 训练集大小\n",
        "val_size = 5000  # 验证集大小\n",
        "generator = torch.Generator().manual_seed(42)  # 固定随机种子，保证可复现\n",
        "train_dataset, val_dataset = torch.utils.data.random_split(  # 随机划分数据集\n",
        "    full_dataset,  # 完整数据集\n",
        "    [train_size, val_size],  # 划分比例\n",
        "    generator=generator  # 随机数生成器\n",
        ")\n",
        "\n",
        "# 查看数据集基本信息\n",
        "print(f\"完整数据集大小: {len(full_dataset)}\")  # 打印完整数据集大小\n",
        "print(f\"训练集大小: {len(train_dataset)}\")  # 打印训练集大小\n",
        "print(f\"验证集大小: {len(val_dataset)}\")  # 打印验证集大小\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.148120Z",
          "start_time": "2025-06-26T01:43:33.145230Z"
        },
        "id": "Hgd60eaT5NcS"
      },
      "outputs": [],
      "source": [
        "def cal_mean_std(ds):\n",
        "    mean = 0.\n",
        "    std = 0.\n",
        "    for img, _ in ds:\n",
        "        mean += img.mean(dim=(1, 2)) #dim=(1, 2)表示在通道维度上求平均\n",
        "        std += img.std(dim=(1, 2))  #dim=(1, 2)表示在通道维度上求标准差\n",
        "    mean /= len(ds)\n",
        "    std /= len(ds)\n",
        "    return mean, std\n",
        "# cal_mean_std(train_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Q9Thjzwd5NcS"
      },
      "outputs": [],
      "source": [
        "# 将划分好的45000训练集和5000验证集给DataLoader\n",
        "# 创建数据加载器\n",
        "batch_size = 64\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True #打乱数据集，每次迭代时，数据集的顺序都会被打乱\n",
        ")\n",
        "\n",
        "val_loader = torch.utils.data.DataLoader(\n",
        "    val_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.152657Z",
          "start_time": "2025-06-26T01:43:33.148120Z"
        },
        "id": "PEzHf4D55NcT"
      },
      "outputs": [],
      "source": [
        "# 定义残差块类\n",
        "import torch.nn as nn\n",
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, stride=1):\n",
        "        super().__init__()\n",
        "\n",
        "        # 第一个卷积层\n",
        "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        # 第二个卷积层\n",
        "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
        "\n",
        "        # 如果输入和输出通道数不同或步长不为1，则需要使用1x1卷积进行调整\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_channels != out_channels:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(out_channels)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        out += self.shortcut(residual) # 残差连接\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out\n",
        "\n",
        "# 定义ResNet18模型\n",
        "class ResNet18(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super().__init__()\n",
        "\n",
        "        # 初始卷积层\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)  # 批量归一化\n",
        "        self.relu = nn.ReLU(inplace=True) # relu 激活函数\n",
        "\n",
        "        # 残差层\n",
        "        self.layer1 = self._make_layer(64, 64, 2, stride=1)\n",
        "        self.layer2 = self._make_layer(64, 128, 2, stride=2)\n",
        "        self.layer3 = self._make_layer(128, 256, 2, stride=2)\n",
        "        self.layer4 = self._make_layer(256, 512, 2, stride=2)\n",
        "\n",
        "        # 全局平均池化和全连接层\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Linear(512, num_classes)\n",
        "\n",
        "    def _make_layer(self, in_channels, out_channels, num_blocks, stride):\n",
        "        \"\"\"\n",
        "        创建一个残差层\n",
        "        :param in_channels: 输入通道数\n",
        "        :param out_channels: 输出通道数\n",
        "        :param num_blocks: 残差块数量\n",
        "        :param stride: 步长\n",
        "        :return: 残差层\n",
        "        \"\"\"\n",
        "        layers = []\n",
        "        # 第一个残差块可能需要调整通道数和特征图大小\n",
        "        layers.append(ResidualBlock(in_channels, out_channels, stride))\n",
        "\n",
        "        # 后续残差块的输入通道数已经是out_channels\n",
        "        for _ in range(1, num_blocks):\n",
        "            layers.append(ResidualBlock(out_channels, out_channels))\n",
        "\n",
        "        return nn.Sequential(*layers) #*解包\n",
        "\n",
        "    def forward(self, x):\n",
        "        # print(f\"输入形状: {x.shape}\")\n",
        "\n",
        "        x = self.conv1(x)\n",
        "        # print(f\"conv1后形状: {x.shape}\")\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        # print(f\"layer1后形状: {x.shape}\")\n",
        "        x = self.layer2(x) #输出shape是[1, 128, 16, 16]\n",
        "        # print(f\"layer2后形状: {x.shape}\")\n",
        "        x = self.layer3(x) #输出shape是[1, 256, 8, 8]\n",
        "        # print(f\"layer3后形状: {x.shape}\")\n",
        "        x = self.layer4(x) #输出shape是[1, 512, 4, 4]\n",
        "        # print(f\"layer4后形状: {x.shape}\")\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        # print(f\"avgpool后形状: {x.shape}\")\n",
        "        x = torch.flatten(x, 1)\n",
        "        # print(f\"flatten后形状: {x.shape}\")\n",
        "        x = self.fc(x)\n",
        "        # print(f\"fc后形状: {x.shape}\")\n",
        "\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.185031Z",
          "start_time": "2025-06-26T01:43:33.152657Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mej-sCPF5NcT",
        "outputId": "642eb385-945a-47a4-8717-2471c1273022"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "批次图像形状: torch.Size([64, 3, 32, 32])\n",
            "批次标签形状: torch.Size([64])\n",
            "----------------------------------------------------------------------------------------------------\n",
            "torch.Size([64, 10])\n"
          ]
        }
      ],
      "source": [
        "# 实例化模型\n",
        "model = ResNet18()\n",
        "\n",
        "# 从train_loader获取第一个批次的数据\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# 查看批次数据的形状\n",
        "print(\"批次图像形状:\", images.shape)\n",
        "print(\"批次标签形状:\", labels.shape)\n",
        "\n",
        "\n",
        "print('-'*100)\n",
        "# 进行前向传播\n",
        "with torch.no_grad():  # 不需要计算梯度\n",
        "    outputs = model(images)\n",
        "\n",
        "\n",
        "print(outputs.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.203053Z",
          "start_time": "2025-06-26T01:43:33.199532Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "LA1oEhZg5NcT",
        "outputId": "44326329-b904-4aff-e055-055143e36cb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "需要求梯度的参数总量: 11173962\n",
            "模型总参数量: 11173962\n",
            "\n",
            "各层参数量明细:\n",
            "conv1.weight: 1728 参数\n",
            "bn1.weight: 64 参数\n",
            "bn1.bias: 64 参数\n",
            "layer1.0.conv1.weight: 36864 参数\n",
            "layer1.0.bn1.weight: 64 参数\n",
            "layer1.0.bn1.bias: 64 参数\n",
            "layer1.0.conv2.weight: 36864 参数\n",
            "layer1.0.bn2.weight: 64 参数\n",
            "layer1.0.bn2.bias: 64 参数\n",
            "layer1.1.conv1.weight: 36864 参数\n",
            "layer1.1.bn1.weight: 64 参数\n",
            "layer1.1.bn1.bias: 64 参数\n",
            "layer1.1.conv2.weight: 36864 参数\n",
            "layer1.1.bn2.weight: 64 参数\n",
            "layer1.1.bn2.bias: 64 参数\n",
            "layer2.0.conv1.weight: 73728 参数\n",
            "layer2.0.bn1.weight: 128 参数\n",
            "layer2.0.bn1.bias: 128 参数\n",
            "layer2.0.conv2.weight: 147456 参数\n",
            "layer2.0.bn2.weight: 128 参数\n",
            "layer2.0.bn2.bias: 128 参数\n",
            "layer2.0.shortcut.0.weight: 8192 参数\n",
            "layer2.0.shortcut.1.weight: 128 参数\n",
            "layer2.0.shortcut.1.bias: 128 参数\n",
            "layer2.1.conv1.weight: 147456 参数\n",
            "layer2.1.bn1.weight: 128 参数\n",
            "layer2.1.bn1.bias: 128 参数\n",
            "layer2.1.conv2.weight: 147456 参数\n",
            "layer2.1.bn2.weight: 128 参数\n",
            "layer2.1.bn2.bias: 128 参数\n",
            "layer3.0.conv1.weight: 294912 参数\n",
            "layer3.0.bn1.weight: 256 参数\n",
            "layer3.0.bn1.bias: 256 参数\n",
            "layer3.0.conv2.weight: 589824 参数\n",
            "layer3.0.bn2.weight: 256 参数\n",
            "layer3.0.bn2.bias: 256 参数\n",
            "layer3.0.shortcut.0.weight: 32768 参数\n",
            "layer3.0.shortcut.1.weight: 256 参数\n",
            "layer3.0.shortcut.1.bias: 256 参数\n",
            "layer3.1.conv1.weight: 589824 参数\n",
            "layer3.1.bn1.weight: 256 参数\n",
            "layer3.1.bn1.bias: 256 参数\n",
            "layer3.1.conv2.weight: 589824 参数\n",
            "layer3.1.bn2.weight: 256 参数\n",
            "layer3.1.bn2.bias: 256 参数\n",
            "layer4.0.conv1.weight: 1179648 参数\n",
            "layer4.0.bn1.weight: 512 参数\n",
            "layer4.0.bn1.bias: 512 参数\n",
            "layer4.0.conv2.weight: 2359296 参数\n",
            "layer4.0.bn2.weight: 512 参数\n",
            "layer4.0.bn2.bias: 512 参数\n",
            "layer4.0.shortcut.0.weight: 131072 参数\n",
            "layer4.0.shortcut.1.weight: 512 参数\n",
            "layer4.0.shortcut.1.bias: 512 参数\n",
            "layer4.1.conv1.weight: 2359296 参数\n",
            "layer4.1.bn1.weight: 512 参数\n",
            "layer4.1.bn1.bias: 512 参数\n",
            "layer4.1.conv2.weight: 2359296 参数\n",
            "layer4.1.bn2.weight: 512 参数\n",
            "layer4.1.bn2.bias: 512 参数\n",
            "fc.weight: 5120 参数\n",
            "fc.bias: 10 参数\n"
          ]
        }
      ],
      "source": [
        "# 统计需要求梯度的参数总量\n",
        "total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "print(f\"需要求梯度的参数总量: {total_params}\")\n",
        "\n",
        "# 统计所有参数总量\n",
        "all_params = sum(p.numel() for p in model.parameters())\n",
        "print(f\"模型总参数量: {all_params}\")\n",
        "\n",
        "# 查看每层参数量明细\n",
        "print(\"\\n各层参数量明细:\")\n",
        "for name, param in model.named_parameters():\n",
        "    print(f\"{name}: {param.numel()} 参数\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLyQq0oe5NcT",
        "outputId": "7a02742f-b2d3-4b12-87d0-08165dd9045b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "294912"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "128*3*3*256"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaTC1a9f5NcU"
      },
      "source": [
        "# 各层参数量明细:\n",
        "conv1.weight: 288 参数 3*3*1*32\n",
        "conv1.bias: 32 参数\n",
        "conv2.weight: 9216 参数 3*3*32*32\n",
        "conv2.bias: 32 参数  \n",
        "conv3.weight: 18432 参数 3*3*32*64\n",
        "conv3.bias: 64 参数\n",
        "conv4.weight: 36864 参数  3*3*64*64\n",
        "conv4.bias: 64 参数\n",
        "conv5.weight: 73728 参数\n",
        "conv5.bias: 128 参数\n",
        "conv6.weight: 147456 参数\n",
        "conv6.bias: 128 参数\n",
        "fc1.weight: 294912 参数 128*3*3*256\n",
        "fc1.bias: 256 参数\n",
        "fc2.weight: 2560 参数\n",
        "fc2.bias: 10 参数"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.217395Z",
          "start_time": "2025-06-26T01:43:33.203561Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "3Q1mhn1Q5NcU",
        "outputId": "c26d1eb7-1c9c-482d-d590-079fe05d3b50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('conv1.weight',\n",
              "              tensor([[[[-3.8511e-02, -1.2163e-01, -3.4913e-02],\n",
              "                        [ 8.2551e-02,  5.1214e-03,  4.9613e-02],\n",
              "                        [ 1.1759e-01, -1.0390e-01,  1.1581e-01]],\n",
              "              \n",
              "                       [[ 5.5646e-02, -6.9734e-02,  8.0898e-02],\n",
              "                        [ 1.2871e-01, -1.4034e-01,  4.7411e-02],\n",
              "                        [-4.8793e-02,  8.2286e-02, -1.9010e-01]],\n",
              "              \n",
              "                       [[-7.3235e-02, -1.4866e-01,  1.1595e-01],\n",
              "                        [ 6.7658e-02, -1.3754e-02, -4.3367e-02],\n",
              "                        [ 1.3961e-01,  1.4187e-01, -1.7645e-01]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 5.9563e-02, -1.1882e-01,  6.0802e-02],\n",
              "                        [-7.0346e-02, -4.2481e-02, -1.6864e-01],\n",
              "                        [ 1.3570e-01, -1.6190e-02,  7.2799e-02]],\n",
              "              \n",
              "                       [[-1.6173e-01,  7.4292e-02,  5.4737e-02],\n",
              "                        [-6.6167e-02,  7.7945e-02, -3.3626e-02],\n",
              "                        [-1.1045e-01, -5.4119e-02, -1.3395e-01]],\n",
              "              \n",
              "                       [[ 1.2505e-01, -9.0471e-02, -7.2001e-02],\n",
              "                        [ 3.2534e-02, -9.6555e-02, -1.0316e-01],\n",
              "                        [ 2.6700e-02, -3.7997e-02,  1.4008e-01]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.1029e-01,  9.0887e-02, -4.4040e-02],\n",
              "                        [ 3.0293e-02,  1.6318e-01,  1.5516e-01],\n",
              "                        [ 1.2304e-01,  1.0859e-01, -7.5821e-02]],\n",
              "              \n",
              "                       [[-2.1093e-02,  3.2666e-02, -7.3878e-02],\n",
              "                        [-1.4559e-01,  2.4653e-03, -1.3107e-01],\n",
              "                        [ 1.3234e-01,  3.4775e-02, -7.2238e-02]],\n",
              "              \n",
              "                       [[-1.0751e-01,  2.0622e-02,  1.4551e-02],\n",
              "                        [ 1.0244e-01,  5.8755e-02, -8.7561e-02],\n",
              "                        [-1.9055e-01,  8.0960e-02, -1.9001e-01]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-1.0003e-01,  1.6189e-01,  1.0002e-02],\n",
              "                        [-1.1674e-01, -9.7249e-02, -5.4834e-02],\n",
              "                        [ 1.7186e-01, -4.0658e-02, -2.3126e-02]],\n",
              "              \n",
              "                       [[-1.3587e-01, -1.4155e-01, -1.6065e-01],\n",
              "                        [ 1.7921e-01,  6.1303e-02, -1.7976e-01],\n",
              "                        [-9.1908e-02,  6.9201e-02,  1.7860e-01]],\n",
              "              \n",
              "                       [[ 1.7005e-01, -7.7477e-02, -6.9268e-02],\n",
              "                        [-7.9329e-02, -1.7112e-01, -1.3881e-01],\n",
              "                        [ 7.1871e-02, -1.8344e-01, -1.6731e-01]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.0435e-01, -4.5318e-02,  6.8823e-02],\n",
              "                        [ 6.7042e-02, -5.7488e-02, -1.0019e-01],\n",
              "                        [ 1.5741e-01,  2.6464e-02,  1.6374e-02]],\n",
              "              \n",
              "                       [[ 9.3759e-02,  1.5451e-01,  1.0845e-02],\n",
              "                        [ 1.3603e-01,  1.7015e-01,  1.1466e-02],\n",
              "                        [ 8.4732e-02, -1.1674e-01, -7.6501e-03]],\n",
              "              \n",
              "                       [[ 9.1755e-02, -3.9409e-02,  7.1315e-02],\n",
              "                        [-1.8935e-01,  6.8408e-02, -3.4878e-02],\n",
              "                        [-1.0797e-01,  7.8609e-02,  7.2008e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.4546e-01,  4.3976e-03,  1.3194e-01],\n",
              "                        [ 6.3762e-02,  7.6921e-02, -1.3434e-01],\n",
              "                        [ 6.9907e-02,  1.1402e-01, -8.6940e-03]],\n",
              "              \n",
              "                       [[-6.5568e-05,  1.0636e-01,  3.8610e-02],\n",
              "                        [-6.1003e-02,  1.7875e-01,  1.1364e-01],\n",
              "                        [ 5.5677e-02, -1.5552e-01, -2.1138e-02]],\n",
              "              \n",
              "                       [[-1.5786e-01,  1.7601e-01,  2.9258e-02],\n",
              "                        [-1.2601e-01, -1.8929e-01,  2.8159e-02],\n",
              "                        [ 1.6248e-01, -1.2680e-01, -2.1583e-02]]]])),\n",
              "             ('bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('bn1.bias',\n",
              "              tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "                      0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "                      0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])),\n",
              "             ('bn1.running_mean',\n",
              "              tensor([-2.1567e-04,  2.0969e-04, -4.3350e-04, -2.3482e-03,  1.6753e-03,\n",
              "                       1.3985e-03,  1.9213e-03,  5.0385e-04, -1.7051e-04,  1.7891e-04,\n",
              "                      -2.3593e-04,  7.3470e-04, -5.5793e-04,  4.3840e-05,  7.4546e-04,\n",
              "                       4.9946e-04,  2.1983e-04, -9.8116e-04,  2.8719e-04, -8.8949e-04,\n",
              "                      -1.3391e-03,  3.0995e-03, -1.2783e-03, -1.7448e-03,  3.8057e-04,\n",
              "                       5.8163e-04, -2.9012e-03,  1.2839e-03, -3.7585e-03,  1.6849e-03,\n",
              "                      -7.2068e-04,  1.2077e-03,  5.8078e-04,  5.6998e-04,  1.0815e-03,\n",
              "                      -9.8401e-05, -1.4608e-03, -4.9105e-04, -1.3002e-03, -3.3511e-04,\n",
              "                      -1.2832e-03, -6.5262e-04, -1.5129e-03, -9.1751e-04,  1.5880e-03,\n",
              "                      -9.8184e-04, -9.5847e-04,  5.0564e-04,  5.4094e-05, -2.5655e-03,\n",
              "                       1.7946e-03,  1.6249e-03, -2.6570e-06, -1.2343e-04, -5.1708e-04,\n",
              "                       6.3575e-04,  8.6247e-04, -8.8826e-04, -3.0198e-04, -1.4606e-03,\n",
              "                      -2.7325e-03,  2.0317e-03,  6.8969e-04,  4.7059e-04])),\n",
              "             ('bn1.running_var',\n",
              "              tensor([0.9077, 0.9318, 0.9152, 0.9427, 0.9280, 0.9138, 0.9120, 0.9251, 0.9028,\n",
              "                      0.9862, 0.9180, 0.9145, 0.9048, 0.9120, 0.9496, 0.9291, 0.9169, 0.9074,\n",
              "                      0.9155, 0.9126, 0.9155, 1.0682, 0.9236, 0.9627, 0.9091, 0.9414, 0.9228,\n",
              "                      1.0550, 1.0309, 0.9117, 0.9308, 0.9563, 0.9358, 0.9197, 0.9195, 0.9054,\n",
              "                      0.9139, 0.9087, 0.9505, 0.9105, 0.9634, 0.9121, 1.0818, 0.9341, 0.9319,\n",
              "                      1.0683, 0.9327, 0.9247, 0.9404, 1.0743, 0.9289, 0.9272, 0.9116, 0.9156,\n",
              "                      0.9396, 0.9263, 0.9301, 0.9085, 0.9121, 0.9136, 0.9461, 1.0089, 0.9310,\n",
              "                      0.9126])),\n",
              "             ('bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.0.conv1.weight',\n",
              "              tensor([[[[ 0.0353,  0.0099, -0.0177],\n",
              "                        [-0.0368,  0.0260, -0.0024],\n",
              "                        [ 0.0032,  0.0227,  0.0118]],\n",
              "              \n",
              "                       [[-0.0228,  0.0225,  0.0191],\n",
              "                        [ 0.0094, -0.0327,  0.0155],\n",
              "                        [ 0.0069, -0.0381,  0.0053]],\n",
              "              \n",
              "                       [[-0.0410,  0.0182,  0.0091],\n",
              "                        [-0.0011, -0.0036, -0.0224],\n",
              "                        [ 0.0092, -0.0126,  0.0399]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0184,  0.0332,  0.0208],\n",
              "                        [ 0.0330,  0.0361, -0.0019],\n",
              "                        [ 0.0181,  0.0179,  0.0306]],\n",
              "              \n",
              "                       [[ 0.0272, -0.0365, -0.0102],\n",
              "                        [ 0.0160, -0.0126, -0.0271],\n",
              "                        [-0.0366,  0.0301, -0.0285]],\n",
              "              \n",
              "                       [[-0.0299,  0.0304, -0.0101],\n",
              "                        [-0.0278,  0.0387, -0.0328],\n",
              "                        [-0.0106,  0.0303, -0.0285]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0095, -0.0025, -0.0083],\n",
              "                        [ 0.0069, -0.0120,  0.0238],\n",
              "                        [-0.0184,  0.0239, -0.0354]],\n",
              "              \n",
              "                       [[ 0.0318,  0.0353, -0.0089],\n",
              "                        [ 0.0330,  0.0070,  0.0010],\n",
              "                        [ 0.0262, -0.0178,  0.0365]],\n",
              "              \n",
              "                       [[-0.0312,  0.0069,  0.0404],\n",
              "                        [ 0.0175,  0.0384, -0.0074],\n",
              "                        [ 0.0189,  0.0381, -0.0412]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0003,  0.0021,  0.0149],\n",
              "                        [-0.0370,  0.0121,  0.0081],\n",
              "                        [-0.0106,  0.0043,  0.0228]],\n",
              "              \n",
              "                       [[ 0.0357, -0.0088, -0.0038],\n",
              "                        [-0.0120, -0.0322, -0.0090],\n",
              "                        [ 0.0324,  0.0395, -0.0300]],\n",
              "              \n",
              "                       [[-0.0296, -0.0333,  0.0204],\n",
              "                        [ 0.0195,  0.0245, -0.0265],\n",
              "                        [-0.0374,  0.0237,  0.0084]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0380, -0.0017,  0.0194],\n",
              "                        [-0.0096,  0.0252,  0.0294],\n",
              "                        [ 0.0288, -0.0349,  0.0021]],\n",
              "              \n",
              "                       [[-0.0074,  0.0342,  0.0229],\n",
              "                        [-0.0203,  0.0093, -0.0145],\n",
              "                        [-0.0113,  0.0228, -0.0126]],\n",
              "              \n",
              "                       [[-0.0277, -0.0413,  0.0131],\n",
              "                        [-0.0279, -0.0163,  0.0026],\n",
              "                        [-0.0103, -0.0081, -0.0219]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0385, -0.0030, -0.0236],\n",
              "                        [ 0.0064, -0.0324,  0.0320],\n",
              "                        [ 0.0054,  0.0182,  0.0313]],\n",
              "              \n",
              "                       [[ 0.0140,  0.0404,  0.0332],\n",
              "                        [ 0.0005,  0.0116, -0.0102],\n",
              "                        [-0.0017, -0.0405,  0.0037]],\n",
              "              \n",
              "                       [[-0.0204,  0.0144,  0.0235],\n",
              "                        [ 0.0195,  0.0300,  0.0109],\n",
              "                        [ 0.0398, -0.0247,  0.0416]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-0.0291,  0.0012,  0.0286],\n",
              "                        [-0.0154, -0.0216,  0.0171],\n",
              "                        [-0.0341, -0.0217,  0.0028]],\n",
              "              \n",
              "                       [[-0.0293,  0.0052,  0.0305],\n",
              "                        [ 0.0397, -0.0108,  0.0403],\n",
              "                        [ 0.0131, -0.0074,  0.0245]],\n",
              "              \n",
              "                       [[ 0.0267,  0.0298,  0.0414],\n",
              "                        [ 0.0036, -0.0043, -0.0003],\n",
              "                        [-0.0405,  0.0110,  0.0199]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0202, -0.0395,  0.0039],\n",
              "                        [ 0.0115,  0.0332, -0.0387],\n",
              "                        [ 0.0275,  0.0174,  0.0058]],\n",
              "              \n",
              "                       [[-0.0072, -0.0253, -0.0269],\n",
              "                        [-0.0377, -0.0003,  0.0245],\n",
              "                        [-0.0280, -0.0162,  0.0311]],\n",
              "              \n",
              "                       [[-0.0392,  0.0109,  0.0353],\n",
              "                        [ 0.0152,  0.0299,  0.0073],\n",
              "                        [-0.0251,  0.0192,  0.0175]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "                        [-0.0172,  0.0167, -0.0254]],\n",
              "              \n",
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              "                        [ 0.0400, -0.0057,  0.0049],\n",
              "                        [ 0.0298, -0.0003,  0.0390]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0311,  0.0167, -0.0060],\n",
              "                        [-0.0274, -0.0139, -0.0117],\n",
              "                        [-0.0322,  0.0148, -0.0380]],\n",
              "              \n",
              "                       [[-0.0196, -0.0300, -0.0156],\n",
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              "              \n",
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              "                        [-0.0334,  0.0317,  0.0128],\n",
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              "              \n",
              "              \n",
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              "                        [ 0.0375, -0.0268,  0.0282],\n",
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              "              \n",
              "                       [[-0.0279, -0.0008, -0.0101],\n",
              "                        [-0.0387, -0.0121, -0.0196],\n",
              "                        [ 0.0084, -0.0186, -0.0056]],\n",
              "              \n",
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              "                        [-0.0369,  0.0103,  0.0082],\n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0132,  0.0204, -0.0224],\n",
              "                        [ 0.0199, -0.0389,  0.0095],\n",
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              "              \n",
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              "                        [-0.0332, -0.0156, -0.0236],\n",
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              "              \n",
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              "                        [ 0.0290,  0.0339,  0.0322],\n",
              "                        [-0.0198, -0.0061, -0.0045]]]])),\n",
              "             ('layer1.0.bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer1.0.bn1.bias',\n",
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              "             ('layer1.0.bn1.running_mean',\n",
              "              tensor([ 0.0264, -0.0143,  0.0039, -0.0066, -0.0368,  0.0404, -0.0201,  0.0626,\n",
              "                      -0.0081, -0.0150, -0.0124,  0.0057,  0.0073,  0.0151,  0.0164, -0.0223,\n",
              "                      -0.0107, -0.0051,  0.0008,  0.0075,  0.0134,  0.0027,  0.0064, -0.0367,\n",
              "                      -0.0246,  0.0276, -0.0073, -0.0072, -0.0132,  0.0052,  0.0070,  0.0094,\n",
              "                      -0.0126,  0.0075,  0.0222,  0.0011,  0.0073, -0.0287,  0.0061,  0.0142,\n",
              "                      -0.0113, -0.0146, -0.0079,  0.0026, -0.0070,  0.0309, -0.0385,  0.0026,\n",
              "                      -0.0319,  0.0105, -0.0411,  0.0160,  0.0200, -0.0066, -0.0687, -0.0205,\n",
              "                      -0.0076, -0.0068,  0.0300,  0.0022,  0.0536,  0.0258, -0.0199, -0.0334])),\n",
              "             ('layer1.0.bn1.running_var',\n",
              "              tensor([0.9100, 0.9174, 0.9106, 0.9079, 0.9085, 0.9132, 0.9054, 0.9237, 0.9074,\n",
              "                      0.9097, 0.9279, 0.9062, 0.9075, 0.9112, 0.9201, 0.9094, 0.9060, 0.9090,\n",
              "                      0.9141, 0.9291, 0.9104, 0.9121, 0.9103, 0.9245, 0.9075, 0.9086, 0.9078,\n",
              "                      0.9109, 0.9303, 0.9284, 0.9159, 0.9155, 0.9059, 0.9157, 0.9236, 0.9064,\n",
              "                      0.9077, 0.9160, 0.9160, 0.9080, 0.9119, 0.9098, 0.9141, 0.9120, 0.9126,\n",
              "                      0.9118, 0.9151, 0.9093, 0.9097, 0.9081, 0.9105, 0.9067, 0.9087, 0.9115,\n",
              "                      0.9136, 0.9067, 0.9159, 0.9111, 0.9067, 0.9098, 0.9091, 0.9078, 0.9116,\n",
              "                      0.9117])),\n",
              "             ('layer1.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.0.conv2.weight',\n",
              "              tensor([[[[ 2.2382e-03, -1.8212e-02, -1.9982e-02],\n",
              "                        [-2.5787e-02,  2.7435e-03,  1.2643e-02],\n",
              "                        [ 1.9105e-02, -8.2752e-03,  1.3334e-02]],\n",
              "              \n",
              "                       [[ 2.4326e-02,  2.3613e-02,  3.1012e-02],\n",
              "                        [-2.5838e-02, -4.0146e-02, -2.2561e-02],\n",
              "                        [-2.7184e-02, -3.6381e-02,  4.0921e-02]],\n",
              "              \n",
              "                       [[-5.3748e-03,  1.1936e-02, -2.7569e-02],\n",
              "                        [ 1.6385e-03,  2.5259e-02, -2.4155e-02],\n",
              "                        [-6.1027e-04,  6.0957e-04,  3.9240e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-4.1355e-02, -3.7897e-02, -3.1324e-02],\n",
              "                        [ 8.2862e-03,  6.1124e-03, -3.3669e-02],\n",
              "                        [ 3.7745e-02,  1.6450e-03, -1.0122e-02]],\n",
              "              \n",
              "                       [[-2.0531e-03, -2.8039e-02,  1.3259e-02],\n",
              "                        [-3.1505e-02,  1.2168e-02, -3.8826e-03],\n",
              "                        [-3.0803e-03, -3.8043e-02, -1.3917e-02]],\n",
              "              \n",
              "                       [[ 2.3510e-04,  2.1824e-02,  3.0048e-02],\n",
              "                        [ 7.3387e-03, -4.1277e-02, -2.1717e-03],\n",
              "                        [-6.1238e-04, -7.6385e-03,  7.1314e-04]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.0678e-03, -2.2292e-02,  1.5665e-02],\n",
              "                        [-2.4295e-02, -2.4253e-02, -2.7758e-02],\n",
              "                        [ 4.0515e-02,  2.2143e-02, -3.0336e-02]],\n",
              "              \n",
              "                       [[-2.2143e-02,  2.9676e-02,  1.7883e-02],\n",
              "                        [-3.5831e-03,  6.6173e-03,  3.2877e-02],\n",
              "                        [-1.2479e-03,  3.8112e-02, -2.5032e-02]],\n",
              "              \n",
              "                       [[ 4.1105e-02,  9.2283e-03,  2.5873e-02],\n",
              "                        [-1.1568e-02,  9.0479e-03, -5.4073e-03],\n",
              "                        [ 1.9032e-02, -1.5653e-02,  3.6569e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.9305e-02, -3.2899e-02,  3.8363e-03],\n",
              "                        [-1.5807e-02, -4.8045e-03,  2.5111e-02],\n",
              "                        [ 2.1785e-02, -3.0069e-02, -3.3651e-02]],\n",
              "              \n",
              "                       [[-1.9477e-02,  1.1722e-02,  3.8132e-02],\n",
              "                        [-1.7539e-02,  3.4529e-02, -3.4262e-02],\n",
              "                        [-6.1239e-03,  3.1172e-02,  1.6602e-02]],\n",
              "              \n",
              "                       [[ 2.6528e-02,  3.4703e-02,  3.8494e-02],\n",
              "                        [-1.0604e-03, -1.1079e-02,  9.1386e-03],\n",
              "                        [-2.1705e-02, -9.6292e-03,  3.8807e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.5695e-02,  1.8846e-02, -3.4772e-02],\n",
              "                        [ 1.5234e-02,  4.1355e-02,  3.6079e-02],\n",
              "                        [-5.2949e-03, -3.3295e-02, -9.9592e-03]],\n",
              "              \n",
              "                       [[-5.8814e-03,  2.1394e-02, -2.1266e-02],\n",
              "                        [ 4.5812e-03,  3.8158e-02,  1.0665e-02],\n",
              "                        [-2.3923e-02, -2.8922e-02, -2.7422e-02]],\n",
              "              \n",
              "                       [[ 3.7552e-02, -1.4936e-03, -1.6072e-03],\n",
              "                        [-4.0935e-03,  3.7997e-02,  1.7607e-03],\n",
              "                        [ 3.2904e-02,  1.0827e-02, -2.1188e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.1679e-02, -1.6997e-02,  3.3293e-02],\n",
              "                        [ 4.0301e-02, -2.1138e-04, -2.6002e-02],\n",
              "                        [-1.9382e-02,  3.8166e-02,  3.4307e-02]],\n",
              "              \n",
              "                       [[-2.8192e-02,  6.2203e-03, -3.8032e-02],\n",
              "                        [-9.9108e-03,  2.5856e-02, -1.0306e-02],\n",
              "                        [-2.2330e-02, -1.8855e-02,  2.6564e-03]],\n",
              "              \n",
              "                       [[ 3.5006e-02, -2.5140e-02,  1.3436e-02],\n",
              "                        [ 1.8139e-02, -3.0475e-04,  3.0908e-03],\n",
              "                        [-2.2934e-02, -8.4423e-03, -1.1535e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 4.0815e-02,  7.9748e-03,  2.4678e-02],\n",
              "                        [ 5.9204e-04,  1.3775e-02,  2.6721e-02],\n",
              "                        [ 3.8277e-02,  6.9564e-03, -3.7440e-02]],\n",
              "              \n",
              "                       [[ 3.8484e-02, -3.3233e-02,  3.7215e-02],\n",
              "                        [ 2.7646e-02, -1.4405e-02,  2.8543e-02],\n",
              "                        [ 1.8887e-02,  6.2300e-03, -3.9514e-02]],\n",
              "              \n",
              "                       [[ 2.5380e-02, -3.1604e-02,  7.2909e-03],\n",
              "                        [-2.1294e-02,  3.2733e-02,  3.2392e-02],\n",
              "                        [-1.0398e-02, -8.8704e-03,  9.3983e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.5147e-02, -3.8879e-02, -1.5734e-02],\n",
              "                        [ 2.6687e-02,  2.7886e-02,  2.0363e-02],\n",
              "                        [ 3.2310e-03, -2.1126e-02,  3.4661e-02]],\n",
              "              \n",
              "                       [[ 3.7068e-02,  1.5761e-02,  1.2298e-02],\n",
              "                        [-5.9286e-03,  2.3675e-02, -1.2474e-02],\n",
              "                        [ 4.1400e-02,  1.1093e-02,  1.6834e-02]],\n",
              "              \n",
              "                       [[-1.2234e-02,  2.8585e-02,  2.3632e-02],\n",
              "                        [ 1.7277e-02, -1.5440e-02,  3.0715e-02],\n",
              "                        [ 4.1508e-02, -1.3846e-02,  1.6552e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.8580e-02, -3.9390e-02, -1.2839e-02],\n",
              "                        [-3.5403e-02,  4.0619e-02, -1.6528e-02],\n",
              "                        [-9.3360e-03,  6.5681e-03,  1.9291e-02]],\n",
              "              \n",
              "                       [[-3.2837e-02, -5.2695e-03,  3.2906e-02],\n",
              "                        [-2.2065e-02,  2.0901e-02,  9.9510e-03],\n",
              "                        [-3.1951e-02, -1.4252e-02, -2.5029e-02]],\n",
              "              \n",
              "                       [[-1.4307e-02,  1.1920e-02,  5.3761e-04],\n",
              "                        [-1.5362e-02,  9.3110e-03,  3.3049e-02],\n",
              "                        [-8.9432e-03, -4.5984e-03,  1.0591e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.8884e-02,  3.8847e-02, -1.5938e-02],\n",
              "                        [-1.2878e-03, -8.4417e-03,  3.0745e-02],\n",
              "                        [-3.4305e-02, -3.9449e-02,  3.7138e-02]],\n",
              "              \n",
              "                       [[ 3.7266e-02,  3.0006e-02,  9.5705e-03],\n",
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              "                        [-1.1704e-02,  8.1614e-04,  1.1599e-02]],\n",
              "              \n",
              "                       [[ 1.7724e-03,  5.2459e-03,  3.5679e-02],\n",
              "                        [-1.4207e-02, -1.6916e-02,  3.1604e-03],\n",
              "                        [-3.1840e-02,  3.0207e-02, -3.0519e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.9047e-02, -3.4145e-02,  1.3173e-02],\n",
              "                        [ 6.6582e-03, -3.9221e-03, -2.8370e-02],\n",
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              "              \n",
              "                       [[-2.4952e-02, -2.8743e-03,  2.9665e-02],\n",
              "                        [-2.7506e-02, -1.6365e-02,  1.2220e-02],\n",
              "                        [ 1.5514e-02,  1.4489e-02,  3.0027e-02]],\n",
              "              \n",
              "                       [[-1.2748e-02, -1.5131e-02,  3.0602e-02],\n",
              "                        [-2.6724e-02,  8.5538e-03,  2.9968e-02],\n",
              "                        [-2.4503e-02, -3.0227e-02,  2.5286e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.9334e-02,  1.2558e-02, -2.3685e-02],\n",
              "                        [-9.5094e-05, -7.4249e-03, -2.1173e-02],\n",
              "                        [ 7.7447e-03,  2.1621e-03,  2.6483e-03]],\n",
              "              \n",
              "                       [[-2.1846e-02, -1.4004e-02,  2.3266e-03],\n",
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              "                        [-2.6301e-03, -1.2116e-03, -2.3451e-02]],\n",
              "              \n",
              "                       [[-2.1725e-02, -2.8898e-02, -3.4060e-03],\n",
              "                        [ 3.7534e-02, -5.6648e-03, -3.7870e-02],\n",
              "                        [ 3.9336e-02, -1.0054e-02,  3.9417e-02]]]])),\n",
              "             ('layer1.0.bn2.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "             ('layer1.0.bn2.bias',\n",
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              "             ('layer1.0.bn2.running_mean',\n",
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              "             ('layer1.0.bn2.running_var',\n",
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              "                      0.9102])),\n",
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              "             ('layer1.1.conv1.weight',\n",
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              "              \n",
              "                       [[ 0.0145, -0.0209, -0.0322],\n",
              "                        [-0.0364,  0.0129, -0.0140],\n",
              "                        [-0.0038, -0.0209, -0.0167]],\n",
              "              \n",
              "                       [[-0.0350, -0.0029,  0.0208],\n",
              "                        [ 0.0156, -0.0279,  0.0407],\n",
              "                        [ 0.0157, -0.0233,  0.0047]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0307,  0.0330,  0.0212],\n",
              "                        [ 0.0382, -0.0277,  0.0408],\n",
              "                        [ 0.0293, -0.0175,  0.0415]],\n",
              "              \n",
              "                       [[-0.0339, -0.0292,  0.0140],\n",
              "                        [ 0.0281, -0.0203,  0.0345],\n",
              "                        [ 0.0102, -0.0219, -0.0283]],\n",
              "              \n",
              "                       [[ 0.0143, -0.0405, -0.0109],\n",
              "                        [-0.0004, -0.0384,  0.0259],\n",
              "                        [-0.0392, -0.0272,  0.0079]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0398, -0.0344, -0.0326],\n",
              "                        [ 0.0062,  0.0129,  0.0122],\n",
              "                        [ 0.0132,  0.0004,  0.0384]],\n",
              "              \n",
              "                       [[-0.0299,  0.0148, -0.0001],\n",
              "                        [ 0.0358,  0.0209,  0.0141],\n",
              "                        [-0.0144,  0.0376, -0.0351]],\n",
              "              \n",
              "                       [[ 0.0071, -0.0168,  0.0016],\n",
              "                        [-0.0150,  0.0400,  0.0183],\n",
              "                        [ 0.0035,  0.0225, -0.0081]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0051, -0.0319, -0.0003],\n",
              "                        [ 0.0130,  0.0123, -0.0236],\n",
              "                        [-0.0116,  0.0377, -0.0378]],\n",
              "              \n",
              "                       [[ 0.0312,  0.0394, -0.0332],\n",
              "                        [-0.0204, -0.0316, -0.0311],\n",
              "                        [-0.0025,  0.0141,  0.0360]],\n",
              "              \n",
              "                       [[ 0.0279, -0.0130, -0.0323],\n",
              "                        [-0.0326, -0.0119,  0.0014],\n",
              "                        [ 0.0415, -0.0355, -0.0021]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0330, -0.0082, -0.0140],\n",
              "                        [-0.0220, -0.0119, -0.0340],\n",
              "                        [-0.0289,  0.0101,  0.0106]],\n",
              "              \n",
              "                       [[-0.0305,  0.0397,  0.0275],\n",
              "                        [ 0.0176,  0.0165,  0.0070],\n",
              "                        [-0.0158,  0.0381,  0.0269]],\n",
              "              \n",
              "                       [[ 0.0367,  0.0040, -0.0352],\n",
              "                        [-0.0350,  0.0120,  0.0080],\n",
              "                        [ 0.0174,  0.0121,  0.0214]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0227, -0.0097, -0.0229],\n",
              "                        [ 0.0119,  0.0361,  0.0063],\n",
              "                        [ 0.0077, -0.0079, -0.0410]],\n",
              "              \n",
              "                       [[-0.0311,  0.0057,  0.0273],\n",
              "                        [ 0.0319,  0.0269, -0.0300],\n",
              "                        [-0.0206,  0.0373, -0.0286]],\n",
              "              \n",
              "                       [[ 0.0141,  0.0357, -0.0387],\n",
              "                        [-0.0288,  0.0093, -0.0304],\n",
              "                        [-0.0030, -0.0376,  0.0077]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0379, -0.0385, -0.0088],\n",
              "                        [ 0.0067, -0.0118,  0.0110],\n",
              "                        [ 0.0148, -0.0033, -0.0092]],\n",
              "              \n",
              "                       [[ 0.0027,  0.0112, -0.0226],\n",
              "                        [ 0.0238,  0.0272, -0.0330],\n",
              "                        [ 0.0199,  0.0261, -0.0197]],\n",
              "              \n",
              "                       [[ 0.0290,  0.0334, -0.0078],\n",
              "                        [-0.0275, -0.0215, -0.0157],\n",
              "                        [ 0.0009,  0.0271, -0.0378]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0183,  0.0135, -0.0302],\n",
              "                        [ 0.0416,  0.0027, -0.0112],\n",
              "                        [-0.0234, -0.0025, -0.0224]],\n",
              "              \n",
              "                       [[-0.0167, -0.0257, -0.0144],\n",
              "                        [-0.0286,  0.0318,  0.0265],\n",
              "                        [ 0.0173, -0.0210, -0.0217]],\n",
              "              \n",
              "                       [[-0.0198, -0.0080,  0.0298],\n",
              "                        [-0.0183, -0.0253, -0.0396],\n",
              "                        [ 0.0005, -0.0070,  0.0076]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0239, -0.0110,  0.0386],\n",
              "                        [-0.0166, -0.0130,  0.0295],\n",
              "                        [-0.0245,  0.0184,  0.0330]],\n",
              "              \n",
              "                       [[-0.0392,  0.0117, -0.0204],\n",
              "                        [-0.0255, -0.0411,  0.0160],\n",
              "                        [-0.0239, -0.0047, -0.0192]],\n",
              "              \n",
              "                       [[-0.0087, -0.0256, -0.0217],\n",
              "                        [ 0.0210,  0.0093, -0.0276],\n",
              "                        [ 0.0355,  0.0148, -0.0179]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0251,  0.0028, -0.0143],\n",
              "                        [-0.0016, -0.0027, -0.0402],\n",
              "                        [-0.0020,  0.0166, -0.0260]],\n",
              "              \n",
              "                       [[ 0.0199,  0.0043, -0.0076],\n",
              "                        [ 0.0086,  0.0234, -0.0043],\n",
              "                        [-0.0093, -0.0209,  0.0265]],\n",
              "              \n",
              "                       [[ 0.0265,  0.0206,  0.0199],\n",
              "                        [-0.0248,  0.0403, -0.0235],\n",
              "                        [ 0.0284, -0.0004, -0.0175]]],\n",
              "              \n",
              "              \n",
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              "                        [-0.0310, -0.0276, -0.0090]],\n",
              "              \n",
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              "                        [-0.0313,  0.0331,  0.0313],\n",
              "                        [ 0.0131,  0.0068,  0.0150]],\n",
              "              \n",
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              "                        [ 0.0047, -0.0228,  0.0388],\n",
              "                        [-0.0162, -0.0011, -0.0241]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0344,  0.0079,  0.0200],\n",
              "                        [ 0.0316, -0.0214, -0.0100],\n",
              "                        [ 0.0268,  0.0277, -0.0137]],\n",
              "              \n",
              "                       [[ 0.0331,  0.0291,  0.0007],\n",
              "                        [ 0.0344, -0.0369,  0.0085],\n",
              "                        [ 0.0090, -0.0037,  0.0055]],\n",
              "              \n",
              "                       [[ 0.0202, -0.0295,  0.0344],\n",
              "                        [-0.0324,  0.0125,  0.0078],\n",
              "                        [-0.0289, -0.0244, -0.0135]]]])),\n",
              "             ('layer1.1.bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer1.1.bn1.bias',\n",
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              "             ('layer1.1.bn1.running_mean',\n",
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              "                       0.0332,  0.0479,  0.0001,  0.0313,  0.0120, -0.0521,  0.0137,  0.0046,\n",
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              "                      -0.0242, -0.0008,  0.0173, -0.0201, -0.0045,  0.0062,  0.0495,  0.0381,\n",
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              "                       0.0069,  0.0460, -0.0501,  0.0523,  0.1052,  0.0036, -0.0581,  0.0295,\n",
              "                       0.0213,  0.0087, -0.0045, -0.0252,  0.0337, -0.0206, -0.0217, -0.0055])),\n",
              "             ('layer1.1.bn1.running_var',\n",
              "              tensor([0.9190, 0.9168, 0.9235, 0.9216, 0.9181, 0.9161, 0.9269, 0.9267, 0.9317,\n",
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              "                      0.9213, 0.9168, 0.9313, 0.9264, 0.9252, 0.9361, 0.9226, 0.9293, 0.9183,\n",
              "                      0.9308, 0.9501, 0.9181, 0.9339, 0.9262, 0.9193, 0.9259, 0.9652, 0.9168,\n",
              "                      0.9240, 0.9258, 0.9155, 0.9176, 0.9160, 0.9147, 0.9195, 0.9158, 0.9182,\n",
              "                      0.9235])),\n",
              "             ('layer1.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.1.conv2.weight',\n",
              "              tensor([[[[-0.0226,  0.0152, -0.0170],\n",
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              "              \n",
              "                       [[-0.0374,  0.0128, -0.0274],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0142, -0.0002, -0.0329]],\n",
              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
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              "                        [-0.0209, -0.0248,  0.0037]],\n",
              "              \n",
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              "                        [-0.0018, -0.0291, -0.0082]],\n",
              "              \n",
              "                       [[ 0.0271, -0.0070, -0.0230],\n",
              "                        [-0.0074,  0.0224, -0.0381],\n",
              "                        [ 0.0078, -0.0180,  0.0406]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0155,  0.0373, -0.0395],\n",
              "                        [-0.0039, -0.0160, -0.0414],\n",
              "                        [-0.0047, -0.0084, -0.0218]],\n",
              "              \n",
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              "                        [ 0.0080,  0.0092,  0.0310]],\n",
              "              \n",
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              "                        [-0.0362,  0.0236, -0.0275]],\n",
              "              \n",
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              "                        [-0.0340,  0.0135, -0.0110]],\n",
              "              \n",
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              "                        [ 0.0347,  0.0179,  0.0313]],\n",
              "              \n",
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              "                        [-0.0356, -0.0002,  0.0112]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
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              "                        [ 0.0319, -0.0366, -0.0137]],\n",
              "              \n",
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              "                        [-0.0346,  0.0202, -0.0317]],\n",
              "              \n",
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              "                        [-0.0267,  0.0300, -0.0016],\n",
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              "              \n",
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              "                        [ 0.0248,  0.0180, -0.0325],\n",
              "                        [-0.0015, -0.0399,  0.0226]],\n",
              "              \n",
              "                       [[ 0.0235, -0.0281, -0.0170],\n",
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              "              \n",
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              "                        [-0.0171, -0.0040,  0.0279]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0179,  0.0398, -0.0376],\n",
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              "              \n",
              "                       ...,\n",
              "              \n",
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              "                        [ 0.0043, -0.0310,  0.0117],\n",
              "                        [ 0.0317,  0.0089, -0.0124]],\n",
              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0019,  0.0025, -0.0332],\n",
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              "             ('layer1.1.bn2.weight',\n",
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              "             ('layer1.1.bn2.bias',\n",
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              "             ('layer1.1.bn2.running_mean',\n",
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              "                      -5.2242e-02,  2.2399e-02, -1.8754e-02,  2.2124e-03,  1.1975e-02,\n",
              "                       1.1043e-02,  3.7875e-02,  1.4378e-02, -4.0887e-03, -1.3489e-02,\n",
              "                      -1.7006e-02, -2.2605e-02, -4.2401e-03, -2.3070e-02, -8.6274e-05,\n",
              "                       4.2503e-02,  1.1115e-02, -2.2573e-02, -9.0837e-03, -1.9199e-02,\n",
              "                      -2.3529e-02, -3.3896e-02,  2.7541e-02,  2.7730e-02,  2.3274e-02,\n",
              "                       1.6960e-02, -4.8012e-03,  4.2955e-02,  3.6310e-02,  3.5628e-02,\n",
              "                      -3.9585e-03, -2.5915e-02,  1.5738e-02, -9.9999e-03, -8.3357e-03,\n",
              "                      -7.2142e-03,  9.3369e-03, -1.0345e-02, -1.4880e-02])),\n",
              "             ('layer1.1.bn2.running_var',\n",
              "              tensor([0.9112, 0.9111, 0.9117, 0.9119, 0.9122, 0.9162, 0.9159, 0.9142, 0.9083,\n",
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              "                      0.9150, 0.9155, 0.9123, 0.9080, 0.9103, 0.9091, 0.9213, 0.9124, 0.9075,\n",
              "                      0.9118])),\n",
              "             ('layer1.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.0.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0385, -0.0215,  0.0045]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0273,  0.0391,  0.0001],\n",
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              "             ('layer2.0.bn1.weight',\n",
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              "             ('layer2.0.bn1.bias',\n",
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              "                       0.0108,  0.0588, -0.0378,  0.0189, -0.0661,  0.0065,  0.0538,  0.0489,\n",
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              "                       0.0870,  0.0639, -0.0097,  0.0480, -0.0051, -0.0114, -0.1148, -0.0537])),\n",
              "             ('layer2.0.bn1.running_var',\n",
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              "                      0.9239, 0.9295, 0.9392, 0.9471, 0.9341, 0.9432, 0.9395, 0.9354, 0.9241,\n",
              "                      0.9515, 0.9418])),\n",
              "             ('layer2.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       [[-1.6965e-02,  2.9418e-02,  2.1368e-02],\n",
              "                        [ 1.5221e-02, -2.8839e-02,  2.3576e-02],\n",
              "                        [-1.5650e-02,  2.5668e-02,  9.9973e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.9078e-02,  4.6029e-03,  6.1543e-03],\n",
              "                        [ 1.4755e-02,  2.5621e-02,  2.9317e-02],\n",
              "                        [-1.2988e-03, -2.7353e-02,  5.2879e-03]],\n",
              "              \n",
              "                       [[-1.6895e-02,  2.3966e-02,  2.1053e-02],\n",
              "                        [-2.4178e-02, -2.8340e-02, -5.3937e-03],\n",
              "                        [ 6.6086e-03, -5.6949e-03, -2.2104e-02]],\n",
              "              \n",
              "                       [[-7.7869e-04,  1.5277e-02, -6.9274e-03],\n",
              "                        [-5.5771e-03, -2.3604e-03,  2.7662e-02],\n",
              "                        [ 1.8900e-02,  2.3210e-02, -2.1296e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 5.2323e-03, -2.8197e-02,  1.6905e-02],\n",
              "                        [-1.7638e-02,  2.4480e-02, -2.7109e-02],\n",
              "                        [-2.7644e-02,  1.9320e-02,  2.5041e-02]],\n",
              "              \n",
              "                       [[ 7.3589e-03, -2.5611e-02, -2.5084e-03],\n",
              "                        [ 9.0260e-03, -1.8285e-02,  1.1850e-02],\n",
              "                        [ 1.9075e-02, -2.2594e-02,  1.8019e-02]],\n",
              "              \n",
              "                       [[ 2.7498e-02, -1.1457e-02, -1.8280e-02],\n",
              "                        [ 2.5006e-02, -2.2222e-02, -7.8083e-03],\n",
              "                        [ 2.1109e-02, -9.0627e-04,  4.7128e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.5450e-02, -3.8610e-03, -2.2922e-02],\n",
              "                        [-5.6664e-03,  7.6356e-03,  5.9007e-03],\n",
              "                        [ 4.4827e-03, -6.9931e-03, -4.0940e-03]],\n",
              "              \n",
              "                       [[-2.6267e-02, -1.1965e-03,  1.0533e-03],\n",
              "                        [ 1.2853e-02,  2.0457e-02,  2.3798e-02],\n",
              "                        [-2.4885e-02,  1.7198e-02, -7.3575e-04]],\n",
              "              \n",
              "                       [[-5.9152e-03,  2.1607e-02,  8.5932e-03],\n",
              "                        [ 1.3097e-02, -2.4425e-03,  2.6826e-02],\n",
              "                        [-2.5241e-02, -4.1751e-03,  1.5737e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.1013e-02,  2.3906e-02,  1.7529e-02],\n",
              "                        [-1.3223e-02,  1.8295e-02,  1.3451e-02],\n",
              "                        [-2.9806e-03,  6.5125e-03, -4.7726e-03]],\n",
              "              \n",
              "                       [[-2.1028e-02, -2.6201e-03, -2.4800e-02],\n",
              "                        [-9.2805e-03, -2.1259e-02,  2.0265e-02],\n",
              "                        [ 6.6948e-03, -1.9655e-02,  2.0393e-02]],\n",
              "              \n",
              "                       [[ 2.5546e-02,  1.6031e-02, -2.8184e-03],\n",
              "                        [-1.8410e-02, -2.4048e-02, -8.9946e-03],\n",
              "                        [-1.1196e-02,  2.5839e-02, -1.9951e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-1.5016e-02, -1.4928e-02, -1.2034e-02],\n",
              "                        [ 1.3395e-02,  1.7942e-02,  3.4500e-03],\n",
              "                        [-1.9487e-02, -1.2873e-02,  1.6998e-02]],\n",
              "              \n",
              "                       [[-2.7369e-02,  1.2410e-02, -2.6036e-02],\n",
              "                        [-1.3271e-02,  1.4696e-02, -1.5921e-02],\n",
              "                        [ 2.0810e-02, -3.5958e-03, -1.5979e-02]],\n",
              "              \n",
              "                       [[ 2.6085e-02, -1.3969e-02,  6.0162e-03],\n",
              "                        [ 2.0429e-02, -2.4181e-02, -1.8680e-02],\n",
              "                        [ 2.0241e-04, -7.9011e-03, -7.6086e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.5763e-02, -5.2543e-03, -5.6882e-03],\n",
              "                        [-1.1259e-02,  1.6357e-04, -6.2023e-03],\n",
              "                        [-1.0521e-02, -2.6731e-02, -2.8612e-02]],\n",
              "              \n",
              "                       [[ 6.0037e-03, -1.0261e-02,  1.1957e-02],\n",
              "                        [ 2.7594e-02, -6.0161e-03,  1.3557e-02],\n",
              "                        [ 1.0676e-02,  1.7869e-02,  1.7867e-02]],\n",
              "              \n",
              "                       [[-2.3554e-02, -7.7021e-03,  2.6157e-02],\n",
              "                        [-2.0915e-02,  7.3828e-03,  1.2457e-02],\n",
              "                        [-1.9878e-02, -5.6595e-03,  2.6481e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.9408e-02, -1.3262e-04,  1.2659e-02],\n",
              "                        [-2.4132e-02,  1.8761e-02,  2.3540e-02],\n",
              "                        [ 1.7200e-02,  2.9064e-02,  1.1091e-03]],\n",
              "              \n",
              "                       [[ 2.0956e-02,  1.6458e-02, -2.0667e-02],\n",
              "                        [ 2.2765e-02,  4.6160e-03, -1.6919e-02],\n",
              "                        [ 8.8188e-03,  2.0024e-03, -1.3742e-03]],\n",
              "              \n",
              "                       [[-2.8294e-02, -1.9051e-03, -5.2762e-03],\n",
              "                        [-6.6247e-03,  9.0726e-03, -2.4652e-02],\n",
              "                        [-3.3366e-03,  3.8520e-03,  4.2625e-05]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.0156e-02, -9.9730e-03, -7.3144e-03],\n",
              "                        [-1.1378e-02,  1.4267e-02, -7.4320e-03],\n",
              "                        [-9.5221e-03,  7.9677e-03,  1.2529e-02]],\n",
              "              \n",
              "                       [[-1.1353e-02, -2.6918e-03,  2.7442e-02],\n",
              "                        [-2.2635e-02,  9.8848e-03,  1.8058e-02],\n",
              "                        [ 9.3960e-03,  3.0187e-03, -1.9516e-02]],\n",
              "              \n",
              "                       [[-2.3540e-02, -2.4417e-02, -2.6049e-02],\n",
              "                        [-7.4541e-03,  4.2807e-03,  9.3693e-03],\n",
              "                        [ 1.9521e-03,  2.5679e-02, -1.2232e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.5952e-02,  1.1257e-02,  6.2920e-03],\n",
              "                        [-1.3893e-02,  2.7833e-02, -1.8950e-02],\n",
              "                        [-1.0738e-02, -2.6305e-02,  9.4030e-03]],\n",
              "              \n",
              "                       [[ 2.0563e-03, -3.6718e-03, -8.3377e-03],\n",
              "                        [-2.7632e-02, -2.3174e-02,  2.8327e-02],\n",
              "                        [-2.8945e-02, -9.7960e-03,  5.2652e-03]],\n",
              "              \n",
              "                       [[ 1.4176e-02,  1.1700e-02,  9.8874e-03],\n",
              "                        [-2.2668e-03,  2.8341e-02, -2.4796e-02],\n",
              "                        [ 6.0026e-03, -2.2531e-02, -2.2711e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.4197e-02, -7.2293e-03,  2.8055e-02],\n",
              "                        [ 9.8563e-03,  2.4448e-03, -1.4534e-02],\n",
              "                        [-2.0087e-02,  1.8669e-02,  1.5614e-03]],\n",
              "              \n",
              "                       [[-2.9187e-02, -1.6035e-02,  9.4004e-03],\n",
              "                        [-2.5763e-02,  2.5124e-03, -2.3375e-02],\n",
              "                        [-1.4000e-02, -8.6510e-03, -8.2933e-04]],\n",
              "              \n",
              "                       [[-2.0362e-02,  8.7406e-04,  2.8127e-02],\n",
              "                        [-1.1461e-02, -1.2622e-02,  2.3738e-02],\n",
              "                        [-2.6139e-02,  2.0061e-02, -1.9540e-02]]]])),\n",
              "             ('layer2.1.bn1.weight',\n",
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              "             ('layer2.1.bn1.bias',\n",
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              "             ('layer2.1.bn1.running_mean',\n",
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              "                      -0.0051, -0.0185,  0.0120,  0.0153, -0.0354, -0.0133,  0.0088, -0.0648])),\n",
              "             ('layer2.1.bn1.running_var',\n",
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              "                      0.9207, 0.9259])),\n",
              "             ('layer2.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.1.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                      ...,\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0145, -0.0242,  0.0079]],\n",
              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "                        [ 0.0055, -0.0198,  0.0223]],\n",
              "              \n",
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              "                        [ 0.0195, -0.0111,  0.0159],\n",
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              "              \n",
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              "              \n",
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              "                        [-0.0185, -0.0244, -0.0021],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "                        [ 0.0190,  0.0271, -0.0155]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0096,  0.0191, -0.0083],\n",
              "                        [ 0.0047,  0.0084,  0.0094]]]])),\n",
              "             ('layer2.1.bn2.weight',\n",
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              "                      1., 1.])),\n",
              "             ('layer2.1.bn2.bias',\n",
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              "             ('layer2.1.bn2.running_mean',\n",
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              "                      -0.0151, -0.0045, -0.0320,  0.0413, -0.0002, -0.0029, -0.0077, -0.0325,\n",
              "                       0.0146, -0.0430,  0.0334,  0.0077,  0.0449,  0.0110,  0.0251,  0.0111])),\n",
              "             ('layer2.1.bn2.running_var',\n",
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              "                      0.9105, 0.9104, 0.9115, 0.9097, 0.9089, 0.9106, 0.9126, 0.9109, 0.9107,\n",
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              "                      0.9103, 0.9135, 0.9115, 0.9092, 0.9109, 0.9089, 0.9115, 0.9175, 0.9125,\n",
              "                      0.9087, 0.9107])),\n",
              "             ('layer2.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer3.0.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer3.0.bn1.weight',\n",
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              "             ('layer3.0.bn1.bias',\n",
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              "                       0.0713, -0.0099,  0.0143,  0.0270, -0.0859, -0.0447, -0.0002, -0.0349,\n",
              "                       0.0852,  0.0296, -0.0071,  0.0343,  0.0077, -0.0284,  0.0277,  0.0121,\n",
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              "                       0.0429,  0.0284, -0.0430,  0.0526,  0.0154,  0.0041, -0.0206,  0.0222,\n",
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              "                      -0.0085, -0.0451, -0.0234,  0.0277, -0.0203,  0.1000,  0.0409, -0.0037,\n",
              "                       0.0705, -0.0084,  0.0354,  0.0656, -0.0126,  0.0278,  0.0168,  0.0273,\n",
              "                       0.0860,  0.0070, -0.0365, -0.0620,  0.0401, -0.0565,  0.0403, -0.0359])),\n",
              "             ('layer3.0.bn1.running_var',\n",
              "              tensor([0.9399, 0.9274, 0.9446, 0.9255, 0.9400, 0.9437, 0.9289, 0.9334, 0.9289,\n",
              "                      0.9272, 0.9368, 0.9506, 0.9385, 0.9320, 0.9397, 0.9325, 0.9383, 0.9404,\n",
              "                      0.9326, 0.9306, 0.9337, 0.9409, 0.9281, 0.9364, 0.9412, 0.9379, 0.9408,\n",
              "                      0.9314, 0.9418, 0.9293, 0.9279, 0.9292, 0.9309, 0.9615, 0.9489, 0.9322,\n",
              "                      0.9250, 0.9378, 0.9295, 0.9375, 0.9373, 0.9378, 0.9272, 0.9280, 0.9428,\n",
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              "                      0.9308, 0.9328, 0.9310, 0.9295, 0.9353, 0.9303, 0.9339, 0.9400, 0.9325,\n",
              "                      0.9364, 0.9316, 0.9573, 0.9237, 0.9342, 0.9334, 0.9327, 0.9533, 0.9335,\n",
              "                      0.9422, 0.9344, 0.9366, 0.9643, 0.9396, 0.9414, 0.9347, 0.9394, 0.9317,\n",
              "                      0.9325, 0.9273, 0.9603, 0.9305, 0.9483, 0.9389, 0.9394, 0.9416, 0.9340,\n",
              "                      0.9274, 0.9397, 0.9367, 0.9307, 0.9355, 0.9297, 0.9394, 0.9293, 0.9342,\n",
              "                      0.9288, 0.9323, 0.9262, 0.9245, 0.9394, 0.9260, 0.9376, 0.9368, 0.9285,\n",
              "                      0.9384, 0.9301, 0.9347, 0.9372, 0.9354, 0.9382, 0.9359, 0.9272, 0.9383,\n",
              "                      0.9349, 0.9285, 0.9361, 0.9361, 0.9324, 0.9312, 0.9276, 0.9350, 0.9271,\n",
              "                      0.9333, 0.9374, 0.9421, 0.9315, 0.9583, 0.9404, 0.9285, 0.9321, 0.9305,\n",
              "                      0.9379, 0.9366, 0.9389, 0.9330, 0.9385, 0.9298, 0.9416, 0.9341, 0.9343,\n",
              "                      0.9295, 0.9309, 0.9608, 0.9377, 0.9315, 0.9593, 0.9275, 0.9361, 0.9288,\n",
              "                      0.9267, 0.9315, 0.9360, 0.9326, 0.9291, 0.9291, 0.9322, 0.9298, 0.9394,\n",
              "                      0.9329, 0.9367, 0.9341, 0.9396, 0.9294, 0.9306, 0.9402, 0.9289, 0.9239,\n",
              "                      0.9584, 0.9312, 0.9453, 0.9319, 0.9433, 0.9291, 0.9432, 0.9514, 0.9376,\n",
              "                      0.9274, 0.9351, 0.9324, 0.9378, 0.9421, 0.9348, 0.9321, 0.9325, 0.9359,\n",
              "                      0.9287, 0.9295, 0.9523, 0.9430, 0.9365, 0.9309, 0.9358, 0.9329, 0.9338,\n",
              "                      0.9365, 0.9412, 0.9360, 0.9360, 0.9558, 0.9451, 0.9298, 0.9388, 0.9290,\n",
              "                      0.9257, 0.9343, 0.9323, 0.9556, 0.9358, 0.9380, 0.9340, 0.9317, 0.9335,\n",
              "                      0.9356, 0.9290, 0.9375, 0.9283, 0.9591, 0.9293, 0.9273, 0.9562, 0.9328,\n",
              "                      0.9277, 0.9269, 0.9340, 0.9341, 0.9358, 0.9287, 0.9333, 0.9309, 0.9300,\n",
              "                      0.9322, 0.9272, 0.9310, 0.9523, 0.9374, 0.9373, 0.9368, 0.9363, 0.9462,\n",
              "                      0.9311, 0.9320, 0.9520, 0.9418, 0.9305, 0.9249, 0.9270, 0.9246, 0.9289,\n",
              "                      0.9320, 0.9313, 0.9375, 0.9390])),\n",
              "             ('layer3.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer3.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer3.0.bn2.weight',\n",
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              "             ('layer3.0.bn2.bias',\n",
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              "             ('layer3.0.bn2.running_mean',\n",
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              "                       5.7012e-03])),\n",
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              "                      0.9253, 0.9184, 0.9202, 0.9220, 0.9233, 0.9206, 0.9255, 0.9199, 0.9279,\n",
              "                      0.9223, 0.9198, 0.9193, 0.9191])),\n",
              "             ('layer3.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer3.1.conv2.weight',\n",
              "              tensor([[[[-4.5783e-03,  7.6639e-03, -9.4104e-03],\n",
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              "              \n",
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              "              \n",
              "                       [[-7.2006e-03,  7.4924e-03, -9.0416e-03],\n",
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              "                        [-7.9738e-03, -1.2975e-02, -4.9880e-04]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.9146e-02,  1.9899e-02,  1.3061e-02],\n",
              "                        [ 8.1842e-03, -3.8280e-03,  1.3289e-02],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       [[ 5.8729e-03,  5.2642e-03, -1.2890e-02],\n",
              "                        [-2.0678e-02,  1.9317e-02, -4.6302e-03],\n",
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              "              \n",
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              "              \n",
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              "                        [-3.1734e-03,  8.1569e-03,  7.7075e-03]]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-3.8799e-03, -2.5306e-03, -1.9272e-02],\n",
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              "              \n",
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              "                        [ 1.4539e-02,  4.6659e-03,  1.1236e-03]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 6.3942e-03,  9.0857e-03,  1.8495e-02],\n",
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              "              \n",
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              "              \n",
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              "                        [-1.8540e-02,  1.3267e-02, -1.1645e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 1.4109e-02, -1.5847e-02, -9.5630e-03]]]])),\n",
              "             ('layer3.1.bn2.weight',\n",
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              "                      -8.4279e-04, -3.6029e-02,  1.7873e-02,  6.1200e-03, -9.0889e-04,\n",
              "                      -7.4477e-03])),\n",
              "             ('layer3.1.bn2.running_var',\n",
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              "                      0.9100, 0.9119, 0.9086, 0.9091])),\n",
              "             ('layer3.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.0.conv1.weight',\n",
              "              tensor([[[[-9.9434e-03, -5.1654e-03,  8.3155e-03],\n",
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              "                        [-1.9544e-02,  1.8005e-03, -2.0474e-02]],\n",
              "              \n",
              "                       [[-1.3127e-02,  1.7304e-03, -3.7453e-04],\n",
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              "              \n",
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              "                        [-9.9644e-03,  1.6387e-02, -8.8386e-04]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.5705e-02,  3.8867e-03,  1.6163e-02],\n",
              "                        [ 9.5620e-03, -1.7116e-02, -7.4578e-03],\n",
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              "              \n",
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              "              \n",
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              "                        [-1.2271e-02,  1.8104e-02,  2.1612e-03]]],\n",
              "              \n",
              "              \n",
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              "                        [-3.9101e-03, -1.1039e-02, -9.1402e-03],\n",
              "                        [-1.8759e-02,  9.0504e-03, -1.0765e-03]],\n",
              "              \n",
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              "                        [-4.7870e-03, -9.3579e-03,  3.8930e-03],\n",
              "                        [ 1.4947e-02,  1.8079e-02,  6.0638e-03]],\n",
              "              \n",
              "                       [[-1.5012e-02, -5.2980e-03, -6.8286e-03],\n",
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              "                        [-1.1220e-03, -4.5607e-03, -1.2907e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 7.5654e-03,  8.4308e-03,  1.3370e-02],\n",
              "                        [-1.2456e-02, -1.0646e-02, -1.0060e-02],\n",
              "                        [-1.6422e-02, -1.2958e-02, -2.3061e-03]],\n",
              "              \n",
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              "                        [-8.0347e-03,  7.8730e-03, -2.6830e-03],\n",
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              "              \n",
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              "                        [-1.5982e-02,  4.3806e-03, -1.0917e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "                        [-4.6081e-04,  2.3554e-03,  1.2418e-02],\n",
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              "              \n",
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              "                        [-6.5774e-03,  5.8812e-03,  1.4183e-02]],\n",
              "              \n",
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              "              \n",
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              "                        [-1.7083e-02, -1.3882e-02, -1.2349e-02],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-5.8862e-03, -1.2801e-02,  8.0949e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.5694e-02, -1.6064e-02,  1.6200e-02],\n",
              "                        [ 1.9594e-02, -5.1450e-03, -1.4493e-02],\n",
              "                        [ 1.9138e-02,  1.8137e-02,  6.7375e-03]],\n",
              "              \n",
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              "              \n",
              "                       [[ 2.0630e-02,  8.8801e-04, -1.2598e-02],\n",
              "                        [ 1.6214e-03,  1.0232e-02,  1.8078e-02],\n",
              "                        [-1.2967e-02, -7.2852e-03,  7.6952e-03]]],\n",
              "              \n",
              "              \n",
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              "                        [ 1.5615e-02, -1.3750e-02,  1.4283e-02],\n",
              "                        [ 3.2412e-03, -1.2030e-02,  1.3868e-02]],\n",
              "              \n",
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              "              \n",
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              "                        [ 1.7780e-02,  4.9314e-03, -2.0567e-02]],\n",
              "              \n",
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              "              \n",
              "                       [[ 1.3255e-02,  1.1716e-02,  1.1847e-02],\n",
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              "              \n",
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              "              \n",
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              "                        [-5.7534e-03,  2.4001e-03, -7.0753e-03],\n",
              "                        [-1.4986e-02,  6.6553e-03, -1.4318e-02]]]])),\n",
              "             ('layer4.0.bn1.weight',\n",
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              "             ('layer4.0.bn1.running_var',\n",
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              "                      0.9296, 0.9343, 0.9282, 0.9288, 0.9284, 0.9300, 0.9273, 0.9267, 0.9295,\n",
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              "                      0.9322, 0.9284, 0.9392, 0.9242, 0.9292, 0.9400, 0.9312, 0.9313, 0.9339,\n",
              "                      0.9407, 0.9304, 0.9280, 0.9297, 0.9468, 0.9302, 0.9319, 0.9375, 0.9611,\n",
              "                      0.9269, 0.9472, 0.9301, 0.9308, 0.9266, 0.9260, 0.9272, 0.9310, 0.9324,\n",
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              "                      0.9368, 0.9263, 0.9392, 0.9589, 0.9350, 0.9317, 0.9312, 0.9268, 0.9305,\n",
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              "                      0.9776, 0.9279, 0.9349, 0.9383, 0.9277, 0.9328, 0.9312, 0.9374, 0.9327,\n",
              "                      0.9441, 0.9380, 0.9306, 0.9426, 0.9276, 0.9296, 0.9322, 0.9254, 0.9300,\n",
              "                      0.9321, 0.9280, 0.9355, 0.9355, 0.9330, 0.9257, 0.9329, 0.9286, 0.9254,\n",
              "                      0.9367, 0.9310, 0.9262, 0.9318, 0.9311, 0.9288, 0.9317, 0.9324, 0.9412,\n",
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              "                      0.9354, 0.9348, 0.9443, 0.9278, 0.9410, 0.9266, 0.9299, 0.9353])),\n",
              "             ('layer4.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       [[-2.0304e-03,  1.1331e-02, -5.5914e-03],\n",
              "                        [ 8.7810e-03, -4.2169e-03,  8.7686e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer4.0.bn2.running_mean',\n",
              "              tensor([-2.0706e-02,  9.1762e-03,  1.3240e-02, -4.9078e-03, -1.1827e-02,\n",
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              "                       1.6027e-02,  2.1635e-02])),\n",
              "             ('layer4.0.bn2.running_var',\n",
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              "                      0.9079, 0.9091, 0.9082, 0.9095, 0.9125, 0.9092, 0.9087, 0.9096, 0.9124,\n",
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              "                      0.9092, 0.9072, 0.9110, 0.9095, 0.9099, 0.9097, 0.9102, 0.9089, 0.9108,\n",
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              "                      0.9086, 0.9098, 0.9087, 0.9116, 0.9094, 0.9125, 0.9118, 0.9073, 0.9100,\n",
              "                      0.9086, 0.9097, 0.9107, 0.9090, 0.9128, 0.9075, 0.9086, 0.9090, 0.9088,\n",
              "                      0.9088, 0.9086, 0.9100, 0.9087, 0.9086, 0.9122, 0.9106, 0.9086, 0.9097,\n",
              "                      0.9119, 0.9108, 0.9081, 0.9105, 0.9115, 0.9129, 0.9081, 0.9081, 0.9093,\n",
              "                      0.9116, 0.9081, 0.9093, 0.9111, 0.9088, 0.9093, 0.9129, 0.9137, 0.9086,\n",
              "                      0.9103, 0.9078, 0.9095, 0.9145, 0.9099, 0.9084, 0.9099, 0.9103, 0.9097,\n",
              "                      0.9124, 0.9091, 0.9094, 0.9089, 0.9087, 0.9089, 0.9211, 0.9078, 0.9083,\n",
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              "                      -4.5738e-02,  6.4020e-03, -4.5542e-03,  7.6653e-02, -1.7650e-02,\n",
              "                       4.6459e-02,  6.2596e-02, -1.3085e-02,  2.9563e-02, -4.5173e-02,\n",
              "                       5.7340e-03, -5.1511e-02,  4.1180e-02, -3.0818e-02, -3.1461e-02,\n",
              "                       1.4426e-02, -5.9975e-02,  2.8930e-02, -2.3584e-03,  6.5671e-02,\n",
              "                       7.9942e-03,  5.0404e-02, -4.8337e-02,  4.1041e-02,  1.2911e-02,\n",
              "                      -1.0219e-01,  1.7335e-02, -2.6314e-02,  5.0471e-02, -1.3860e-02,\n",
              "                       1.4318e-02, -5.9653e-02,  3.2365e-02, -4.2772e-02,  3.2178e-02,\n",
              "                       3.2395e-02,  7.7010e-03,  2.1805e-02, -3.2009e-02,  6.3641e-02,\n",
              "                       2.2172e-02,  3.2796e-02, -2.0400e-02, -4.7228e-02,  5.3299e-02,\n",
              "                      -3.6709e-02,  4.6884e-02,  9.3131e-02,  1.1863e-01,  4.6682e-02,\n",
              "                       4.5760e-02, -4.5594e-02, -4.6381e-02, -6.2752e-02,  3.0842e-03,\n",
              "                       1.7503e-03, -1.8548e-02, -2.5954e-02, -3.1399e-02,  1.8721e-02,\n",
              "                      -4.4218e-02,  2.0755e-02,  7.4735e-02,  4.4913e-03,  1.6062e-01,\n",
              "                       8.8465e-04, -8.8480e-02,  2.0216e-02, -2.7856e-02,  4.4623e-02,\n",
              "                      -2.3547e-02, -3.2908e-02, -5.2541e-02, -5.4674e-02,  4.2181e-02,\n",
              "                       1.5423e-02, -2.1301e-02,  1.1110e-02, -6.7800e-02, -4.3556e-02,\n",
              "                      -3.2860e-02, -5.8884e-02, -2.5241e-02,  2.0762e-02,  1.2155e-02,\n",
              "                       2.3153e-02, -1.6054e-02,  1.8167e-02,  3.0767e-02, -9.4393e-04,\n",
              "                       1.9834e-03, -2.2564e-02, -2.0780e-03, -1.3741e-01,  9.4253e-03,\n",
              "                       2.9139e-03,  1.7290e-02, -9.9602e-02, -2.0475e-02, -3.9542e-02,\n",
              "                       3.7121e-02, -5.1414e-02, -2.3910e-02,  8.1403e-03,  3.3504e-02,\n",
              "                      -2.4705e-02, -1.3972e-01, -3.9924e-03, -7.8748e-02, -3.4270e-02,\n",
              "                       1.0360e-02, -2.0214e-02,  3.7094e-02,  3.2445e-02, -6.3514e-02,\n",
              "                       2.7279e-02, -2.0534e-03,  7.0004e-02,  5.8487e-02,  1.8174e-02,\n",
              "                       4.3006e-02, -5.3357e-02, -8.1206e-02, -5.8348e-02, -1.0417e-02,\n",
              "                      -1.3734e-02,  4.5713e-02, -1.2539e-02,  9.9694e-03,  7.7220e-02,\n",
              "                       4.6361e-02,  3.0365e-02,  9.0725e-02,  4.0273e-02, -6.0077e-02,\n",
              "                       4.4549e-02, -8.1849e-03,  3.8124e-02,  3.1582e-02, -1.5703e-02,\n",
              "                      -6.6820e-02, -6.9949e-02, -4.7708e-02, -8.7448e-03,  1.7803e-02,\n",
              "                       4.9864e-02,  2.6187e-02])),\n",
              "             ('layer4.0.shortcut.1.running_var',\n",
              "              tensor([0.9366, 0.9414, 0.9412, 0.9290, 0.9396, 0.9319, 0.9295, 0.9339, 0.9348,\n",
              "                      0.9332, 0.9339, 0.9334, 0.9350, 0.9373, 0.9380, 0.9312, 0.9333, 0.9389,\n",
              "                      0.9320, 0.9308, 0.9307, 0.9286, 0.9326, 0.9297, 0.9613, 0.9307, 0.9370,\n",
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              "                      0.9273, 0.9330, 0.9343, 0.9352, 0.9405, 0.9287, 0.9307, 0.9394, 0.9281,\n",
              "                      0.9316, 0.9305, 0.9289, 0.9361, 0.9414, 0.9482, 0.9375, 0.9304, 0.9323,\n",
              "                      0.9315, 0.9323, 0.9367, 0.9341, 0.9233, 0.9301, 0.9325, 0.9302, 0.9340,\n",
              "                      0.9392, 0.9470, 0.9344, 0.9328, 0.9281, 0.9275, 0.9280, 0.9253, 0.9264,\n",
              "                      0.9408, 0.9286, 0.9347, 0.9382, 0.9314, 0.9326, 0.9414, 0.9279, 0.9286,\n",
              "                      0.9245, 0.9361, 0.9314, 0.9236, 0.9325, 0.9330, 0.9267, 0.9253, 0.9340,\n",
              "                      0.9293, 0.9333, 0.9375, 0.9364, 0.9295, 0.9316, 0.9338, 0.9296, 0.9302,\n",
              "                      0.9292, 0.9311, 0.9399, 0.9327, 0.9269, 0.9602, 0.9870, 0.9464, 0.9263,\n",
              "                      0.9352, 0.9352, 0.9360, 0.9338, 0.9341, 0.9511, 0.9353, 0.9406, 0.9288,\n",
              "                      0.9400, 0.9307, 0.9358, 0.9410, 0.9272, 0.9470, 0.9324, 0.9303, 0.9307,\n",
              "                      0.9264, 0.9306, 0.9339, 0.9310, 0.9380, 0.9329, 0.9508, 0.9355, 0.9333,\n",
              "                      0.9342, 0.9405, 0.9318, 0.9313, 0.9298, 0.9331, 0.9372, 0.9400, 0.9358,\n",
              "                      0.9274, 0.9352, 0.9300, 0.9368, 0.9467, 0.9475, 0.9316, 0.9785, 0.9289,\n",
              "                      0.9392, 0.9340, 0.9426, 0.9391, 0.9291, 0.9281, 0.9271, 0.9268, 0.9271,\n",
              "                      0.9370, 0.9358, 0.9294, 0.9312, 0.9352, 0.9439, 0.9370, 0.9355, 0.9344,\n",
              "                      0.9323, 0.9282, 0.9251, 0.9335, 0.9403, 0.9291, 0.9249, 0.9294, 0.9297,\n",
              "                      0.9352, 0.9319, 0.9591, 0.9318, 0.9377, 0.9310, 0.9224, 0.9369, 0.9301,\n",
              "                      0.9350, 0.9328, 0.9314, 0.9523, 0.9467, 0.9423, 0.9305, 0.9279, 0.9378,\n",
              "                      0.9605, 0.9392, 0.9245, 0.9298, 0.9306, 0.9284, 0.9278, 0.9292, 0.9290,\n",
              "                      0.9494, 0.9321, 0.9346, 0.9350, 0.9306, 0.9288, 0.9570, 0.9319, 0.9270,\n",
              "                      0.9292, 0.9337, 0.9311, 0.9312, 0.9399, 0.9269, 0.9315, 0.9457, 0.9307,\n",
              "                      0.9291, 0.9275, 0.9378, 0.9645, 0.9318, 0.9459, 0.9289, 0.9481, 0.9348,\n",
              "                      0.9357, 0.9333, 0.9352, 0.9291, 0.9354, 0.9286, 0.9342, 0.9344, 0.9386,\n",
              "                      0.9258, 0.9308, 0.9319, 0.9455, 0.9364, 0.9321, 0.9335, 0.9297, 0.9396,\n",
              "                      0.9333, 0.9610, 0.9287, 0.9353, 0.9292, 0.9368, 0.9425, 0.9307, 0.9270,\n",
              "                      0.9335, 0.9287, 0.9292, 0.9528, 0.9354, 0.9312, 0.9308, 0.9366, 0.9292,\n",
              "                      0.9316, 0.9309, 0.9365, 0.9272, 0.9260, 0.9295, 0.9310, 0.9334, 0.9264,\n",
              "                      0.9264, 0.9459, 0.9450, 0.9444, 0.9607, 0.9369, 0.9326, 0.9341, 0.9353,\n",
              "                      0.9363, 0.9442, 0.9283, 0.9349, 0.9277, 0.9328, 0.9275, 0.9364, 0.9473,\n",
              "                      0.9419, 0.9299, 0.9287, 0.9278, 0.9446, 0.9288, 0.9300, 0.9287, 0.9419,\n",
              "                      0.9328, 0.9532, 0.9376, 0.9299, 0.9497, 0.9282, 0.9333, 0.9445, 0.9688,\n",
              "                      0.9293, 0.9596, 0.9361, 0.9417, 0.9311, 0.9485, 0.9242, 0.9680, 0.9370,\n",
              "                      0.9540, 0.9293, 0.9363, 0.9323, 0.9423, 0.9271, 0.9306, 0.9271, 0.9316,\n",
              "                      0.9341, 0.9329, 0.9284, 0.9333, 0.9281, 0.9641, 0.9318, 0.9269, 0.9320,\n",
              "                      0.9454, 0.9362, 0.9304, 0.9288, 0.9314, 0.9423, 0.9297, 0.9289, 0.9268,\n",
              "                      0.9330, 0.9397, 0.9298, 0.9342, 0.9591, 0.9290, 0.9304, 0.9256, 0.9337,\n",
              "                      0.9269, 0.9249, 0.9499, 0.9253, 0.9315, 0.9344, 0.9421, 0.9502, 0.9330,\n",
              "                      0.9372, 0.9343, 0.9428, 0.9370, 0.9277, 0.9467, 0.9502, 0.9367, 0.9649,\n",
              "                      0.9286, 0.9252, 0.9308, 0.9480, 0.9412, 0.9322, 0.9659, 0.9277, 0.9294,\n",
              "                      0.9378, 0.9356, 0.9286, 0.9272, 0.9405, 0.9351, 0.9371, 0.9275, 0.9318,\n",
              "                      0.9311, 0.9363, 0.9324, 0.9374, 0.9294, 0.9303, 0.9330, 0.9488, 0.9629,\n",
              "                      0.9484, 0.9270, 0.9305, 0.9287, 0.9348, 0.9233, 0.9344, 0.9319, 0.9437,\n",
              "                      0.9407, 0.9275, 0.9438, 0.9313, 0.9377, 0.9330, 0.9636, 0.9262, 0.9431,\n",
              "                      0.9405, 0.9336, 0.9545, 0.9342, 0.9344, 0.9270, 0.9359, 0.9274, 0.9302,\n",
              "                      0.9363, 0.9266, 0.9313, 0.9294, 0.9356, 0.9260, 0.9457, 0.9331, 0.9376,\n",
              "                      0.9288, 0.9296, 0.9307, 0.9550, 0.9262, 0.9287, 0.9235, 0.9267, 0.9565,\n",
              "                      0.9297, 0.9260, 0.9294, 0.9457, 0.9354, 0.9299, 0.9279, 0.9511, 0.9329,\n",
              "                      0.9333, 0.9328, 0.9336, 0.9371, 0.9277, 0.9402, 0.9352, 0.9311, 0.9391,\n",
              "                      0.9290, 0.9289, 0.9313, 0.9261, 0.9267, 0.9441, 0.9420, 0.9370, 0.9285,\n",
              "                      0.9302, 0.9364, 0.9491, 0.9334, 0.9365, 0.9385, 0.9347, 0.9320, 0.9450,\n",
              "                      0.9329, 0.9306, 0.9661, 0.9253, 0.9320, 0.9323, 0.9304, 0.9428, 0.9254,\n",
              "                      0.9294, 0.9330, 0.9336, 0.9322, 0.9299, 0.9291, 0.9292, 0.9256])),\n",
              "             ('layer4.0.shortcut.1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.1.conv1.weight',\n",
              "              tensor([[[[ 1.2727e-02, -9.7037e-03, -9.5928e-03],\n",
              "                        [ 4.7548e-04, -3.9324e-03,  9.9840e-03],\n",
              "                        [ 4.0404e-03,  1.6097e-04, -5.1755e-03]],\n",
              "              \n",
              "                       [[-9.9401e-03,  9.0453e-03, -1.0867e-02],\n",
              "                        [ 1.2419e-02, -6.7810e-03, -6.5246e-03],\n",
              "                        [ 1.1498e-04, -4.8688e-03, -9.4764e-03]],\n",
              "              \n",
              "                       [[ 7.1573e-04, -1.0540e-02,  1.2322e-02],\n",
              "                        [ 5.9595e-03,  9.2291e-03, -1.4142e-02],\n",
              "                        [ 2.7346e-03, -7.3126e-03,  4.8885e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-8.6936e-03, -8.6234e-03, -6.0432e-03],\n",
              "                        [ 4.1851e-04,  4.8710e-03,  3.3605e-03],\n",
              "                        [ 3.1283e-03,  3.0124e-03, -3.9800e-03]],\n",
              "              \n",
              "                       [[-1.2547e-02, -5.5156e-03,  5.2716e-03],\n",
              "                        [ 1.4473e-02,  1.0415e-02,  1.0097e-02],\n",
              "                        [-6.6992e-03, -4.6706e-03, -1.4751e-03]],\n",
              "              \n",
              "                       [[-1.4337e-03, -2.2974e-03,  1.6947e-05],\n",
              "                        [ 5.0390e-03,  4.5716e-03, -1.0618e-02],\n",
              "                        [-6.9982e-03,  1.1365e-02, -1.0479e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 4.8607e-04, -4.4902e-03,  1.9202e-04],\n",
              "                        [-6.8303e-03, -1.0175e-02, -2.7829e-03],\n",
              "                        [ 4.8971e-03, -7.0141e-03, -6.2130e-03]],\n",
              "              \n",
              "                       [[ 1.3749e-02,  5.0202e-03, -1.0515e-02],\n",
              "                        [ 9.7155e-03, -1.0247e-02,  7.5028e-03],\n",
              "                        [-6.7721e-03,  1.2892e-02,  5.2794e-03]],\n",
              "              \n",
              "                       [[ 7.3736e-03,  1.2274e-02,  7.3359e-03],\n",
              "                        [-1.5750e-03, -1.9731e-03,  1.1206e-02],\n",
              "                        [ 2.3602e-04, -4.4923e-03, -1.2806e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 6.2459e-03,  1.2340e-02, -4.2196e-03],\n",
              "                        [-5.2883e-04, -7.8792e-03,  1.3168e-02],\n",
              "                        [-1.1588e-02, -2.6499e-03,  1.4310e-02]],\n",
              "              \n",
              "                       [[-1.3831e-02,  1.6643e-03,  6.9091e-03],\n",
              "                        [ 9.4341e-03, -5.1204e-03,  3.7483e-03],\n",
              "                        [-1.2364e-02, -1.5804e-04, -8.6123e-04]],\n",
              "              \n",
              "                       [[ 1.0644e-02, -1.2366e-02, -6.1479e-04],\n",
              "                        [-1.3723e-03, -1.1019e-02, -8.4647e-03],\n",
              "                        [-1.3065e-02,  3.4666e-03,  2.9335e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.3508e-02, -6.2475e-03, -1.3940e-02],\n",
              "                        [ 1.4317e-02, -7.5994e-03,  5.9496e-03],\n",
              "                        [-1.2593e-02, -2.3852e-03,  9.7157e-03]],\n",
              "              \n",
              "                       [[ 2.7349e-03,  3.7249e-03,  3.6304e-03],\n",
              "                        [-6.9251e-03, -1.1721e-02,  5.7292e-05],\n",
              "                        [ 5.3512e-03, -3.8077e-03, -1.9155e-03]],\n",
              "              \n",
              "                       [[ 5.9337e-03, -1.9717e-03,  1.4621e-02],\n",
              "                        [-3.2255e-03, -1.1549e-02,  1.2481e-02],\n",
              "                        [ 5.3748e-03, -2.5569e-04, -9.2535e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-3.1412e-03, -1.2260e-03, -8.8235e-03],\n",
              "                        [ 6.1376e-03, -4.4686e-03, -3.8695e-03],\n",
              "                        [ 7.1187e-03, -5.7653e-03, -2.5897e-03]],\n",
              "              \n",
              "                       [[ 3.7454e-03,  7.3498e-03,  5.1741e-03],\n",
              "                        [ 7.0343e-03,  1.4630e-02, -6.1759e-03],\n",
              "                        [ 7.8751e-03,  4.9398e-03, -3.8108e-03]],\n",
              "              \n",
              "                       [[ 5.9567e-03, -5.5319e-04, -7.1181e-03],\n",
              "                        [-1.2634e-02,  7.5349e-03,  1.3522e-02],\n",
              "                        [-1.4220e-02, -1.1716e-02, -1.4565e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-1.2691e-02, -5.3792e-03,  9.9712e-03],\n",
              "                        [-3.0717e-03,  1.0166e-02, -7.1298e-03],\n",
              "                        [-8.1980e-03,  8.4161e-04, -1.3761e-02]],\n",
              "              \n",
              "                       [[-5.3002e-03, -8.6436e-03,  3.4533e-03],\n",
              "                        [ 9.7073e-03,  4.8330e-03,  4.9370e-03],\n",
              "                        [ 4.1753e-03,  9.5438e-03, -4.6836e-03]],\n",
              "              \n",
              "                       [[ 5.2037e-03, -1.0566e-02,  6.8480e-03],\n",
              "                        [-2.4709e-03, -1.2608e-02, -3.7654e-03],\n",
              "                        [-3.1888e-03, -3.7525e-03, -6.6476e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-8.8316e-03, -5.4715e-03,  9.3423e-03],\n",
              "                        [-1.1821e-02,  5.8138e-03, -4.8554e-03],\n",
              "                        [ 1.2677e-02,  3.9502e-03, -1.4062e-02]],\n",
              "              \n",
              "                       [[ 3.9448e-04, -2.3344e-03,  1.1731e-02],\n",
              "                        [-7.8003e-03, -9.3098e-03,  6.3397e-03],\n",
              "                        [-6.1486e-03,  1.0767e-03,  1.1509e-02]],\n",
              "              \n",
              "                       [[ 7.3705e-03,  2.0193e-03,  1.3367e-02],\n",
              "                        [ 4.2813e-03, -1.4195e-02,  1.0937e-02],\n",
              "                        [ 1.1851e-02, -5.4914e-03, -1.4093e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.4087e-02,  6.1423e-03,  7.6038e-03],\n",
              "                        [ 8.5193e-03,  1.2045e-02,  1.8121e-03],\n",
              "                        [ 1.1171e-02, -7.7876e-03, -1.2457e-02]],\n",
              "              \n",
              "                       [[-1.3508e-02, -2.1164e-03, -1.0765e-02],\n",
              "                        [-9.4748e-03,  1.4227e-02,  1.3579e-02],\n",
              "                        [-2.3983e-03, -1.0578e-02,  1.1571e-02]],\n",
              "              \n",
              "                       [[-5.3895e-03,  1.1715e-04,  9.8791e-03],\n",
              "                        [-4.5539e-03, -2.7277e-03, -6.9673e-03],\n",
              "                        [-2.0211e-03, -7.4783e-03,  1.3498e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-9.3684e-03, -6.5872e-04, -1.2295e-02],\n",
              "                        [ 9.5601e-03, -9.9673e-03,  1.0711e-02],\n",
              "                        [-1.2180e-02, -1.1795e-02, -3.5594e-03]],\n",
              "              \n",
              "                       [[-1.1147e-02, -1.0453e-02,  9.6737e-03],\n",
              "                        [-3.0156e-03, -2.2814e-03,  9.8649e-03],\n",
              "                        [-1.3845e-02, -4.9171e-03,  8.8486e-03]],\n",
              "              \n",
              "                       [[ 8.0041e-03,  1.3524e-02, -7.0505e-03],\n",
              "                        [ 5.5422e-03,  1.4550e-02,  3.1007e-03],\n",
              "                        [ 2.7697e-03,  1.2875e-02,  1.4207e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-6.3843e-04,  1.1225e-02,  9.7910e-03],\n",
              "                        [ 1.1802e-02, -1.4367e-02,  1.1298e-02],\n",
              "                        [ 7.3036e-03,  9.8688e-03, -1.0878e-02]],\n",
              "              \n",
              "                       [[-8.6926e-03, -1.9646e-03,  3.2816e-03],\n",
              "                        [ 1.1501e-02,  8.9046e-03, -1.0312e-03],\n",
              "                        [-2.9216e-03,  7.8370e-03, -3.2374e-03]],\n",
              "              \n",
              "                       [[ 2.2249e-03,  8.6775e-03, -9.5841e-03],\n",
              "                        [ 4.4845e-03,  5.7591e-03,  2.4997e-03],\n",
              "                        [ 3.5644e-03,  5.1824e-03,  5.3204e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.6965e-03,  3.9584e-03, -1.2653e-02],\n",
              "                        [-1.0880e-02,  1.0266e-02,  5.1337e-03],\n",
              "                        [-1.1761e-02,  2.1269e-03, -1.3353e-02]],\n",
              "              \n",
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              "                        [-1.1290e-02,  4.4760e-03, -1.5928e-03]],\n",
              "              \n",
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              "                        [-5.4395e-03, -1.1636e-02,  1.3553e-02],\n",
              "                        [-7.2480e-03, -1.2874e-02,  9.4637e-03]]]])),\n",
              "             ('layer4.1.bn1.weight',\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer4.1.bn1.bias',\n",
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              "             ('layer4.1.bn1.running_mean',\n",
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              "                       0.0047, -0.0323,  0.0183,  0.0395, -0.0175,  0.0457,  0.0201, -0.0022,\n",
              "                       0.0211,  0.0186, -0.0190, -0.0036, -0.0251,  0.0273,  0.0339, -0.0095,\n",
              "                      -0.0312,  0.0122, -0.0154,  0.0276,  0.0343, -0.0183,  0.0318, -0.0154,\n",
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              "                       0.0009, -0.0247,  0.0208,  0.0340,  0.0109, -0.0049, -0.0260, -0.0180,\n",
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              "                       0.0093,  0.0091,  0.0333, -0.0124,  0.0562,  0.0204,  0.0357, -0.0131,\n",
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              "                       0.0157, -0.0050, -0.0132,  0.0343,  0.0010, -0.0011,  0.0270,  0.0470,\n",
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              "                       0.0173, -0.0204,  0.0416,  0.0105,  0.0077,  0.0246, -0.0247,  0.0012,\n",
              "                       0.0024,  0.0082, -0.0281,  0.0093, -0.0035,  0.0328,  0.0203,  0.0309,\n",
              "                       0.0009, -0.0325, -0.0211, -0.0067, -0.0108,  0.0343, -0.0587, -0.0353,\n",
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              "                       0.0035,  0.0034,  0.0168,  0.0176,  0.0106, -0.0102, -0.0455, -0.0039,\n",
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              "                      -0.0173,  0.0061, -0.0040,  0.0095, -0.0044,  0.0056, -0.0320, -0.0182,\n",
              "                       0.0002, -0.0284, -0.0086, -0.0394,  0.0037,  0.0049, -0.0227, -0.0049])),\n",
              "             ('layer4.1.bn1.running_var',\n",
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              "                      0.9191, 0.9250, 0.9182, 0.9245, 0.9175, 0.9281, 0.9169, 0.9227, 0.9293,\n",
              "                      0.9185, 0.9165, 0.9155, 0.9180, 0.9180, 0.9200, 0.9187, 0.9310, 0.9168,\n",
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              "                      0.9189, 0.9178, 0.9184, 0.9180, 0.9217, 0.9254, 0.9146, 0.9177, 0.9161,\n",
              "                      0.9178, 0.9219, 0.9201, 0.9312, 0.9187, 0.9184, 0.9198, 0.9237, 0.9167,\n",
              "                      0.9192, 0.9146, 0.9184, 0.9197, 0.9311, 0.9211, 0.9184, 0.9202, 0.9160,\n",
              "                      0.9159, 0.9236, 0.9255, 0.9182, 0.9169, 0.9210, 0.9301, 0.9195, 0.9170,\n",
              "                      0.9191, 0.9175, 0.9206, 0.9180, 0.9193, 0.9222, 0.9205, 0.9211, 0.9246,\n",
              "                      0.9183, 0.9314, 0.9145, 0.9173, 0.9183, 0.9197, 0.9206, 0.9182, 0.9186,\n",
              "                      0.9167, 0.9170, 0.9167, 0.9168, 0.9221, 0.9178, 0.9265, 0.9179, 0.9240,\n",
              "                      0.9222, 0.9165, 0.9202, 0.9249, 0.9177, 0.9169, 0.9189, 0.9191, 0.9165,\n",
              "                      0.9151, 0.9160, 0.9195, 0.9168, 0.9129, 0.9189, 0.9185, 0.9205, 0.9186,\n",
              "                      0.9163, 0.9165, 0.9178, 0.9176, 0.9171, 0.9173, 0.9278, 0.9242, 0.9200,\n",
              "                      0.9177, 0.9156, 0.9192, 0.9191, 0.9202, 0.9192, 0.9230, 0.9155, 0.9168,\n",
              "                      0.9139, 0.9175, 0.9176, 0.9190, 0.9194, 0.9198, 0.9190, 0.9141, 0.9160,\n",
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              "                      0.9176, 0.9209, 0.9190, 0.9206, 0.9190, 0.9172, 0.9182, 0.9187, 0.9187,\n",
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              "             ('layer4.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.1.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                      0., 0., 0., 0., 0., 0., 0., 0.])),\n",
              "             ('layer4.1.bn2.running_mean',\n",
              "              tensor([-1.1172e-02,  1.3465e-02,  1.7089e-02, -1.3032e-02,  1.2478e-02,\n",
              "                      -2.0292e-02, -5.8102e-03, -2.1203e-02,  4.4011e-03, -4.5295e-03,\n",
              "                       2.2341e-02,  7.0693e-03, -3.4966e-02, -1.6007e-03,  5.5840e-04,\n",
              "                      -4.0199e-02, -3.1043e-02, -1.4497e-02,  2.1000e-02, -2.6947e-03,\n",
              "                      -4.6070e-03,  1.2286e-02, -1.5831e-02, -5.6658e-03,  6.7987e-03,\n",
              "                       1.6702e-02, -8.1982e-03, -6.9498e-03, -6.9631e-04,  6.4665e-03,\n",
              "                      -4.7231e-03,  5.8536e-03, -2.6177e-02,  1.1218e-02, -1.1996e-02,\n",
              "                       9.1255e-03, -2.1549e-02, -1.1499e-02,  1.2209e-02,  5.8236e-03,\n",
              "                      -1.3577e-02,  7.0534e-03,  2.3442e-02,  1.2079e-02, -1.1987e-03,\n",
              "                      -1.3742e-02,  5.6650e-03, -1.8651e-02, -2.0118e-02, -2.7486e-03,\n",
              "                       3.5752e-03,  8.9425e-04,  1.0255e-02,  6.2872e-03,  8.8226e-03,\n",
              "                       1.0692e-02, -2.7428e-03, -7.8429e-03, -1.5565e-02, -1.1458e-02,\n",
              "                       7.1581e-03, -2.9921e-02,  2.7597e-02,  2.6135e-02,  1.9612e-02,\n",
              "                       1.1146e-02, -5.7334e-03, -1.5750e-02,  1.4970e-02,  2.4933e-03,\n",
              "                      -7.0077e-03,  2.4887e-03,  3.1243e-02, -4.5484e-03,  4.6458e-02,\n",
              "                      -1.1066e-02, -9.8691e-03, -2.0600e-03,  6.1975e-03, -2.7268e-03,\n",
              "                       2.0180e-02,  1.0426e-02, -1.2411e-03, -8.3560e-03, -1.0934e-02,\n",
              "                      -1.6719e-02,  1.7000e-02,  1.2324e-02,  2.8863e-03,  6.5719e-03,\n",
              "                      -2.7841e-02, -2.0748e-03, -8.6669e-03,  7.5673e-03,  3.5089e-03,\n",
              "                      -1.2521e-02,  1.1288e-02,  1.6147e-02,  5.7326e-03,  3.0699e-03,\n",
              "                       1.0803e-02, -2.6408e-02, -4.8413e-03,  1.3221e-02,  3.3465e-02,\n",
              "                       2.4750e-02, -5.5504e-03,  1.1443e-02,  2.9018e-03,  1.0214e-02,\n",
              "                       7.9388e-03,  3.0026e-04, -4.4975e-04,  4.2281e-03,  4.8368e-03,\n",
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              "                      -1.7741e-02, -3.4370e-03,  2.9989e-03,  1.2421e-02,  2.2594e-02,\n",
              "                       6.7997e-03, -3.2388e-04,  3.4906e-03,  2.3122e-02,  3.6696e-02,\n",
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              "                       3.0402e-02,  5.5343e-03,  7.4033e-03,  9.4988e-04, -2.4952e-02,\n",
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              "                       7.5485e-03, -1.8210e-02,  2.1512e-02,  1.4177e-02, -1.5074e-02,\n",
              "                      -1.0167e-02, -2.3427e-03,  3.9476e-03,  9.6800e-03,  2.5944e-02,\n",
              "                      -7.6038e-04, -6.2943e-03,  8.2617e-03,  1.3378e-02, -1.3306e-03,\n",
              "                       1.3292e-03,  6.9024e-03, -1.6859e-02,  2.3635e-02,  2.7390e-02,\n",
              "                       2.1770e-02,  1.0927e-02,  2.0556e-03, -6.7919e-03, -1.6019e-02,\n",
              "                       1.5477e-03,  1.8455e-02,  1.7231e-02,  4.5619e-03, -8.4514e-03,\n",
              "                       5.4745e-03, -4.6355e-03,  4.1346e-02, -4.5104e-04, -1.6949e-02,\n",
              "                      -4.4686e-02, -1.7376e-02,  1.1742e-02, -1.0129e-02,  4.6471e-03,\n",
              "                       1.7454e-02, -2.1223e-02,  1.5306e-02, -2.0781e-02, -9.1763e-03,\n",
              "                       3.0390e-02,  7.8219e-03, -1.6636e-02,  9.0542e-03,  1.1898e-03,\n",
              "                       4.1051e-03, -1.0550e-02, -3.3569e-02, -6.4021e-03,  1.0887e-02,\n",
              "                       3.4372e-02,  1.4923e-02,  1.6834e-03,  2.6797e-04, -2.6400e-02,\n",
              "                       3.5948e-03, -3.0635e-02, -1.6475e-03, -7.2643e-03,  1.0500e-02,\n",
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              "                      -1.7909e-03, -1.7805e-02, -1.4520e-02, -6.3465e-03, -5.7066e-04,\n",
              "                       1.4942e-03, -2.4855e-02,  1.7491e-02, -8.7462e-03,  1.4147e-02,\n",
              "                       6.2641e-04,  3.5839e-03,  1.7239e-02,  3.5525e-02,  1.4937e-02,\n",
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              "                       2.4184e-02,  3.3072e-02, -3.3738e-02, -3.2872e-02,  6.2696e-03,\n",
              "                      -1.1587e-02,  1.1063e-03, -6.9744e-03, -9.5541e-03,  4.2579e-03,\n",
              "                      -2.0212e-03, -1.5892e-02, -5.0971e-03, -3.6132e-02,  1.3118e-02,\n",
              "                      -3.0791e-02, -4.2444e-02,  3.9468e-03,  6.1895e-04,  2.1154e-02,\n",
              "                       8.7435e-03, -3.2785e-02, -9.7390e-03,  9.0896e-03,  1.8758e-02,\n",
              "                       7.5395e-04, -3.2138e-03, -6.5966e-03,  8.3367e-03, -2.0690e-02,\n",
              "                       3.8575e-03, -2.5577e-02,  1.1829e-03,  1.6217e-02,  5.9493e-04,\n",
              "                       7.0881e-03, -1.8443e-02,  5.8591e-03, -6.6811e-03,  1.6289e-02,\n",
              "                      -1.0641e-02,  1.9472e-03, -4.3194e-03, -1.1238e-02, -1.0911e-02,\n",
              "                       7.4258e-03,  1.0649e-02, -1.3557e-02,  2.7168e-02, -2.3514e-03,\n",
              "                       4.7181e-03,  2.6181e-03,  5.3878e-02,  1.9695e-02, -8.0443e-03,\n",
              "                      -9.8644e-04,  9.1297e-03, -5.8893e-03, -1.6363e-02, -7.5044e-03,\n",
              "                       1.9591e-02, -1.4968e-02,  2.4908e-02, -1.2564e-02,  1.4477e-03,\n",
              "                      -2.7658e-03,  8.4451e-03,  9.2931e-03,  1.2914e-02,  2.2831e-02,\n",
              "                       2.0007e-03,  1.6823e-02,  1.9245e-02,  2.0877e-02, -4.6592e-03,\n",
              "                       3.1819e-02,  3.4536e-02,  6.4252e-04,  8.3717e-03,  1.0483e-03,\n",
              "                      -1.1972e-02,  1.9628e-02, -1.6049e-02,  2.9108e-03, -9.8554e-03,\n",
              "                       1.1490e-02, -1.0495e-03, -3.9251e-04,  3.9339e-02,  7.4690e-03,\n",
              "                      -3.1698e-02, -6.8882e-03, -2.7193e-02,  1.0549e-02,  8.3407e-03,\n",
              "                       1.3988e-02, -7.5154e-03, -3.2413e-02,  6.9474e-03,  3.5660e-03,\n",
              "                       4.9197e-03, -8.1946e-03,  1.2477e-04,  1.2302e-02,  2.1477e-02,\n",
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              "                      -2.5131e-03,  1.8057e-04, -2.0882e-02, -1.2219e-02,  7.1814e-03,\n",
              "                      -8.4467e-03, -2.5699e-02, -6.5421e-03,  3.3798e-03,  2.2352e-02,\n",
              "                       1.1878e-02,  1.0214e-02, -3.6916e-02,  5.8964e-03, -3.8794e-03,\n",
              "                       7.9537e-03,  6.5747e-03,  1.0909e-02, -2.1196e-02,  1.9827e-02,\n",
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              "                       8.2029e-03,  2.4241e-02, -1.4009e-02,  1.2023e-02, -1.0723e-03,\n",
              "                       5.7870e-03, -2.0187e-02,  8.8518e-03,  8.8037e-03, -1.9624e-02,\n",
              "                       1.0155e-02, -1.9653e-02,  2.0872e-03, -1.9553e-02, -2.2378e-02,\n",
              "                       1.2406e-02,  9.8252e-03,  2.4401e-03,  6.0974e-05,  3.0384e-03,\n",
              "                       1.2117e-02,  1.6299e-02, -2.8540e-02,  2.6472e-02, -4.0238e-03,\n",
              "                      -3.2570e-02, -8.9795e-03, -8.0614e-03,  8.9728e-03,  6.5933e-03,\n",
              "                      -2.2797e-03, -5.0143e-03,  4.0668e-03,  5.7192e-03, -8.9845e-03,\n",
              "                       3.1934e-03,  1.1816e-02, -3.6257e-02, -1.2505e-02, -1.8678e-02,\n",
              "                      -3.0990e-02,  8.9088e-03, -1.1938e-02,  1.8761e-02, -1.0877e-02,\n",
              "                      -1.4751e-02, -2.1865e-03,  7.5982e-03, -1.5743e-02, -3.5160e-02,\n",
              "                       1.3341e-02,  3.3938e-03,  2.9772e-02,  2.0603e-02,  1.2073e-02,\n",
              "                      -9.6480e-03,  3.1709e-02,  2.1876e-02,  4.7188e-03,  6.9028e-03,\n",
              "                      -1.0337e-02,  2.0880e-02,  1.3130e-02, -2.5354e-03,  1.2069e-02,\n",
              "                      -1.6456e-02, -1.6921e-02,  8.2949e-04,  1.3412e-03, -1.8797e-02,\n",
              "                       1.7858e-02,  4.0795e-03, -2.3835e-02,  1.2271e-02,  5.8159e-03,\n",
              "                       8.6307e-03, -1.5339e-02, -1.8933e-02,  1.1881e-02, -2.1984e-02,\n",
              "                       4.8052e-03, -1.7388e-02,  2.4578e-02, -1.3226e-02, -3.8703e-02,\n",
              "                       2.4188e-02, -4.6257e-03,  2.8776e-02,  5.4062e-03,  3.6428e-02,\n",
              "                      -1.5780e-02,  7.1558e-03,  1.0756e-02, -2.0842e-02, -2.6814e-02,\n",
              "                      -1.8930e-04, -1.5830e-02])),\n",
              "             ('layer4.1.bn2.running_var',\n",
              "              tensor([0.9079, 0.9080, 0.9108, 0.9122, 0.9093, 0.9089, 0.9097, 0.9078, 0.9077,\n",
              "                      0.9095, 0.9090, 0.9094, 0.9087, 0.9102, 0.9073, 0.9134, 0.9118, 0.9088,\n",
              "                      0.9095, 0.9101, 0.9074, 0.9094, 0.9080, 0.9125, 0.9105, 0.9099, 0.9069,\n",
              "                      0.9102, 0.9100, 0.9098, 0.9116, 0.9081, 0.9112, 0.9085, 0.9074, 0.9083,\n",
              "                      0.9133, 0.9105, 0.9097, 0.9095, 0.9089, 0.9087, 0.9099, 0.9123, 0.9090,\n",
              "                      0.9105, 0.9113, 0.9137, 0.9108, 0.9071, 0.9088, 0.9083, 0.9118, 0.9163,\n",
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              "                      0.9091, 0.9111, 0.9085, 0.9091, 0.9100, 0.9089, 0.9096, 0.9082, 0.9077,\n",
              "                      0.9077, 0.9129, 0.9088, 0.9075, 0.9089, 0.9117, 0.9103, 0.9091, 0.9100,\n",
              "                      0.9082, 0.9088, 0.9099, 0.9148, 0.9119, 0.9115, 0.9109, 0.9084, 0.9120,\n",
              "                      0.9082, 0.9101, 0.9075, 0.9103, 0.9079, 0.9077, 0.9089, 0.9096, 0.9095,\n",
              "                      0.9086, 0.9075, 0.9161, 0.9109, 0.9098, 0.9106, 0.9123, 0.9087, 0.9084,\n",
              "                      0.9101, 0.9089, 0.9079, 0.9093, 0.9100, 0.9092, 0.9086, 0.9106, 0.9101,\n",
              "                      0.9116, 0.9092, 0.9093, 0.9142, 0.9096, 0.9115, 0.9081, 0.9086, 0.9120,\n",
              "                      0.9086, 0.9100, 0.9075, 0.9089, 0.9133, 0.9088, 0.9097, 0.9128, 0.9087,\n",
              "                      0.9104, 0.9079, 0.9083, 0.9150, 0.9100, 0.9095, 0.9093, 0.9075, 0.9099,\n",
              "                      0.9110, 0.9084, 0.9075, 0.9120, 0.9112, 0.9118, 0.9073, 0.9078, 0.9104,\n",
              "                      0.9134, 0.9096, 0.9086, 0.9084, 0.9095, 0.9093, 0.9103, 0.9076, 0.9084,\n",
              "                      0.9085, 0.9121, 0.9111, 0.9097, 0.9085, 0.9104, 0.9088, 0.9101, 0.9111,\n",
              "                      0.9076, 0.9090, 0.9093, 0.9081, 0.9082, 0.9109, 0.9115, 0.9084, 0.9086,\n",
              "                      0.9098, 0.9064, 0.9164, 0.9098, 0.9073, 0.9090, 0.9091, 0.9077, 0.9081,\n",
              "                      0.9140, 0.9089, 0.9078, 0.9105, 0.9094, 0.9066, 0.9088, 0.9079, 0.9078,\n",
              "                      0.9149, 0.9072, 0.9106, 0.9080, 0.9096, 0.9079, 0.9127, 0.9103, 0.9103,\n",
              "                      0.9113, 0.9124, 0.9080, 0.9081, 0.9119, 0.9095, 0.9088, 0.9125, 0.9079,\n",
              "                      0.9131, 0.9091, 0.9119, 0.9118, 0.9074, 0.9074, 0.9103, 0.9091, 0.9118,\n",
              "                      0.9079, 0.9094, 0.9088, 0.9084, 0.9090, 0.9106, 0.9072, 0.9084, 0.9132,\n",
              "                      0.9068, 0.9100, 0.9075, 0.9104, 0.9122, 0.9117, 0.9087, 0.9096, 0.9155,\n",
              "                      0.9135, 0.9109, 0.9112, 0.9102, 0.9119, 0.9081, 0.9072, 0.9076, 0.9070,\n",
              "                      0.9092, 0.9092, 0.9086, 0.9089, 0.9118, 0.9084, 0.9133, 0.9154, 0.9089,\n",
              "                      0.9134, 0.9096, 0.9077, 0.9077, 0.9090, 0.9185, 0.9085, 0.9109, 0.9115,\n",
              "                      0.9091, 0.9081, 0.9121, 0.9086, 0.9087, 0.9084, 0.9077, 0.9122, 0.9105,\n",
              "                      0.9088, 0.9089, 0.9086, 0.9109, 0.9119, 0.9109, 0.9109, 0.9087, 0.9081,\n",
              "                      0.9076, 0.9086, 0.9102, 0.9074, 0.9091, 0.9083, 0.9106, 0.9081, 0.9120,\n",
              "                      0.9080, 0.9079, 0.9080, 0.9099, 0.9087, 0.9095, 0.9090, 0.9078, 0.9093,\n",
              "                      0.9101, 0.9073, 0.9128, 0.9091, 0.9126, 0.9112, 0.9108, 0.9095, 0.9085,\n",
              "                      0.9084, 0.9077, 0.9082, 0.9081, 0.9110, 0.9100, 0.9106, 0.9089, 0.9115,\n",
              "                      0.9100, 0.9093, 0.9079, 0.9141, 0.9128, 0.9087, 0.9124, 0.9090, 0.9109,\n",
              "                      0.9072, 0.9103, 0.9094, 0.9082, 0.9086, 0.9081, 0.9084, 0.9098, 0.9082,\n",
              "                      0.9079, 0.9106, 0.9112, 0.9081, 0.9093, 0.9113, 0.9115, 0.9097, 0.9096,\n",
              "                      0.9078, 0.9083, 0.9088, 0.9083, 0.9143, 0.9072, 0.9080, 0.9084, 0.9082,\n",
              "                      0.9106, 0.9103, 0.9096, 0.9110, 0.9118, 0.9096, 0.9081, 0.9077])),\n",
              "             ('layer4.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('fc.weight',\n",
              "              tensor([[ 0.0309,  0.0158,  0.0310,  ..., -0.0406, -0.0262, -0.0123],\n",
              "                      [-0.0409, -0.0227,  0.0084,  ..., -0.0155,  0.0053,  0.0101],\n",
              "                      [-0.0044,  0.0128,  0.0284,  ..., -0.0031,  0.0161, -0.0063],\n",
              "                      ...,\n",
              "                      [ 0.0049,  0.0306, -0.0232,  ...,  0.0213,  0.0029,  0.0165],\n",
              "                      [ 0.0390, -0.0195, -0.0333,  ..., -0.0201, -0.0068, -0.0419],\n",
              "                      [-0.0246, -0.0045, -0.0238,  ...,  0.0144, -0.0095, -0.0420]])),\n",
              "             ('fc.bias',\n",
              "              tensor([ 0.0146,  0.0099, -0.0300,  0.0080, -0.0273,  0.0297,  0.0117, -0.0173,\n",
              "                      -0.0059,  0.0316]))])"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.state_dict()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IN8ZwGB15NcU"
      },
      "source": [
        "# 设置交叉熵损失函数，SGD优化器"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:40.023837Z",
          "start_time": "2025-06-26T01:43:40.019952Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LedSNgyr5NcU",
        "outputId": "328c4c40-a422-45ba-cc81-3319b217155e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "损失函数: CrossEntropyLoss()\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# 定义损失函数和优化器\n",
        "loss_fn = nn.CrossEntropyLoss()  # 交叉熵损失函数，适用于多分类问题，里边会做softmax，还有会把0-9标签转换成one-hot编码\n",
        "\n",
        "print(\"损失函数:\", loss_fn)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:40.035848Z",
          "start_time": "2025-06-26T01:43:40.032419Z"
        },
        "id": "8fPVMyVF5NcU"
      },
      "outputs": [],
      "source": [
        "model = ResNet18()\n",
        "\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)  # SGD优化器，学习率为0.01，动量为0.9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.732814Z",
          "start_time": "2025-06-26T01:43:40.035848Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "ef78a3b535584b1ba135516a2895558f",
            "b919620f25e64eedb2d5662fa5d75129",
            "1a67bc37408b4011aac8511e073657f2",
            "15e42cf22c7342fd99a009dd088dc2ff",
            "3d9c26556b66458d928530085a3a369b",
            "25ab3239b37b4ae78037726d6cc6ede4",
            "79993b65ab95413aa5218d11481596bb",
            "44863b4e70e84fb6974e7e8b2977ace0",
            "22c73c1d9ae74ef6a7d2aaf15b81cfba",
            "f2e4a3987f944f3eb900d4fee93d4f5d",
            "e30c5ff5c0f04a9e9ac833993d12133e"
          ]
        },
        "id": "e4fwl-iD5NcU",
        "outputId": "bee89afc-60c9-4a89-b3f8-fa55f5c146bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "使用设备: cuda:0\n",
            "训练开始，共训练35200步\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef78a3b535584b1ba135516a2895558f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/35200 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "早停触发! 最佳验证准确率(如果是回归，则是损失): 79.5600\n",
            "早停: 在12000步时，验证准确率没有提升！\n"
          ]
        }
      ],
      "source": [
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"使用设备: {device}\")\n",
        "model = model.to(device) #将模型移动到GPU\n",
        "early_stopping=EarlyStopping(patience=5, delta=0.001)\n",
        "model_saver=ModelSaver(save_dir='model_weights', save_best_only=True)\n",
        "\n",
        "\n",
        "model, history = train_classification_model(model, train_loader, val_loader, loss_fn, optimizer, device, num_epochs=50,\n",
        "early_stopping=early_stopping, model_saver=model_saver, tensorboard_logger=None)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.737721Z",
          "start_time": "2025-06-26T01:45:37.732814Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "uIqGEOAS5NcU",
        "outputId": "443365c4-d57e-4c06-81e1-a192e992d7b5"
      },
      "outputs": [
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        "history['train'][-100:-1]"
      ]
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      "cell_type": "code",
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      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.741226Z",
          "start_time": "2025-06-26T01:45:37.737721Z"
        },
        "colab": {
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        "id": "GdWNKsyw5NcV",
        "outputId": "ee719ce1-923c-4293-b878-a880b58d060d"
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        "history['val'][-1000:-1]"
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        },
        "colab": {
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          "height": 465
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        "id": "_CGgaNC35NcV",
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      "outputs": [
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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_learning_curves(history, sample_step=500)  #横坐标是 steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.818553Z",
          "start_time": "2025-06-26T01:45:37.816716Z"
        },
        "id": "YjdIzbiH5NcV"
      },
      "outputs": [],
      "source": [
        "# 创建一个SevenZipFile对象，用于读取'./test.7z'压缩包\n",
        "a = py7zr.SevenZipFile(r'./test.7z', 'r')\n",
        "# 将压缩包中的所有文件解压到'./competitions/cifar-10/'目录下\n",
        "a.extractall(path=r'./competitions/cifar-10/')\n",
        "# 关闭SevenZipFile对象，释放资源\n",
        "a.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAMSX1aW5NcV",
        "outputId": "16f0ec23-cd54-4c36-ae7f-622ad19cdb1a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:624: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "正在预测测试集...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r预测进度:   0%|          | 0/2344 [00:00<?, ?it/s]/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:624: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(\n",
            "预测进度: 100%|██████████| 2344/2344 [02:08<00:00, 18.28it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id列是否有重复值: False\n",
            "预测完成，结果已保存至 cifar10_submission.csv\n"
          ]
        }
      ],
      "source": [
        "# 导入所需库\n",
        "import os\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "import tqdm\n",
        "\n",
        "# 定义测试数据集类\n",
        "class CIFAR10TestDataset(Dataset):\n",
        "    def __init__(self, img_dir, transform=None):\n",
        "        \"\"\"\n",
        "        初始化测试数据集\n",
        "\n",
        "        参数:\n",
        "            img_dir: 测试图片目录\n",
        "            transform: 图像预处理变换\n",
        "        \"\"\"\n",
        "        self.img_dir = img_dir\n",
        "        self.transform = transform\n",
        "        self.img_files = [f for f in os.listdir(img_dir) if f.endswith('.png')]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.img_files)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = os.path.join(self.img_dir, self.img_files[idx])\n",
        "        image = Image.open(img_path).convert('RGB')\n",
        "\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        # 提取图像ID（文件名去掉扩展名）\n",
        "        img_id = int(os.path.splitext(self.img_files[idx])[0])\n",
        "\n",
        "        return image, img_id\n",
        "\n",
        "# 定义预测函数\n",
        "def predict_test_set(model, img_dir, labels_file, device, batch_size=64):\n",
        "    \"\"\"\n",
        "    预测测试集并生成提交文件\n",
        "\n",
        "    参数:\n",
        "        model: 训练好的模型\n",
        "        img_dir: 测试图片目录\n",
        "        labels_file: 提交模板文件路径\n",
        "        device: 计算设备\n",
        "        batch_size: 批处理大小\n",
        "    \"\"\"\n",
        "    # 图像预处理变换（与训练集相同）\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.4917, 0.4823, 0.4467), (0.2024, 0.1995, 0.2010))\n",
        "    ])\n",
        "\n",
        "    # 创建测试数据集和数据加载器\n",
        "    test_dataset = CIFAR10TestDataset(img_dir, transform=transform)\n",
        "    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
        "\n",
        "    # 设置模型为评估模式\n",
        "    model.eval()\n",
        "\n",
        "    # 读取提交模板\n",
        "    submission_df = pd.read_csv(labels_file)\n",
        "    predictions = {}\n",
        "\n",
        "    # 使用tqdm显示进度条\n",
        "    print(\"正在预测测试集...\")\n",
        "    with torch.no_grad():\n",
        "        for images, img_ids in tqdm.tqdm(test_loader, desc=\"预测进度\"):\n",
        "            images = images.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs, 1) #取最大的索引，作为预测结果\n",
        "\n",
        "            # 记录每个图像的预测结果\n",
        "            for i, img_id in enumerate(img_ids):\n",
        "                predictions[img_id.item()] = predicted[i].item() #因为一个批次有多个图像，所以需要predicted[i]\n",
        "\n",
        "    # 定义类别名称\n",
        "    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "    # 将数值标签转换为类别名称\n",
        "    labeled_predictions = {img_id: class_names[pred] for img_id, pred in predictions.items()}\n",
        "\n",
        "    # 直接创建DataFrame\n",
        "    submission_df = pd.DataFrame({\n",
        "        'id': list(labeled_predictions.keys()),\n",
        "        'label': list(labeled_predictions.values())\n",
        "    })\n",
        "    # 按id列排序\n",
        "    submission_df = submission_df.sort_values(by='id')\n",
        "\n",
        "    # 检查id列是否有重复值\n",
        "    has_duplicates = submission_df['id'].duplicated().any()\n",
        "    print(f\"id列是否有重复值: {has_duplicates}\")\n",
        "    # 保存预测结果\n",
        "    output_file = 'cifar10_submission.csv'\n",
        "    submission_df.to_csv(output_file, index=False)\n",
        "    print(f\"预测完成，结果已保存至 {output_file}\")\n",
        "\n",
        "# 执行测试集预测\n",
        "img_dir = r\"competitions/cifar-10/test\"\n",
        "labels_file = r\"./sampleSubmission.csv\"\n",
        "predict_test_set(model, img_dir, labels_file, device, batch_size=128)\n"
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